NP URI: www.nobleprog.com.ec/apache-arrow/cursos/guayaquil Undefined array key "mysql_identifier_quote_character" /apps/nobleprog-website/includes/functions/new-modules-general-functions.php:82 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/new-modules-general-functions.php [line] => 82 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "mysql_identifier_quote_character" [2] => /apps/nobleprog-website/includes/functions/new-modules-general-functions.php [3] => 82 ) ) [1] => Array ( [file] => /apps/hitra7/drupal7/includes/database/mysql/database.inc [line] => 397 [function] => variable_get [args] => Array ( [0] => mysql_identifier_quote_character [1] => ` ) ) [2] => Array ( [file] => /apps/hitra7/drupal7/includes/database/database.inc [line] => 329 [function] => setPrefix [class] => DatabaseConnection_mysql [object] => DatabaseConnection_mysql Object ( [target:protected] => [key:protected] => [logger:protected] => [transactionLayers:protected] => Array ( ) [driverClasses:protected] => Array ( ) [statementClass:protected] => DatabaseStatementBase [transactionSupport:protected] => 1 [transactionalDDLSupport:protected] => [temporaryNameIndex:protected] => 0 [connection:protected] => [connectionOptions:protected] => Array ( [driver] => mysql [database] => common_fe [username] => root [password] => asdf [host] => localhost [prefix] => Array ( [default] => ) ) [schema:protected] => [prefixes:protected] => Array ( [default] => ) [prefixSearch:protected] => Array ( [0] => { [1] => } ) [prefixReplace:protected] => Array ( [0] => [1] => ) [escapedNames:protected] => Array ( ) [escapedAliases:protected] => Array ( ) [unprefixedTablesMap:protected] => Array ( ) [needsCleanup:protected] => [reservedKeyWords:DatabaseConnection_mysql:private] => Array ( [0] => accessible [1] => add [2] => admin [3] => all [4] => alter [5] => analyze [6] => and [7] => as [8] => asc [9] => asensitive [10] => before [11] => between [12] => bigint [13] => binary [14] => blob [15] => both [16] => by [17] => call [18] => cascade [19] => case [20] => change [21] => char [22] => character [23] => check [24] => collate [25] => column [26] => condition [27] => constraint [28] => continue [29] => convert [30] => create [31] => cross [32] => cube [33] => cume_dist [34] => current_date [35] => current_time [36] => current_timestamp [37] => current_user [38] => cursor [39] => database [40] => databases [41] => day_hour [42] => day_microsecond [43] => day_minute [44] => day_second [45] => dec [46] => decimal [47] => declare [48] => default [49] => delayed [50] => delete [51] => dense_rank [52] => desc [53] => describe [54] => deterministic [55] => distinct [56] => distinctrow [57] => div [58] => double [59] => drop [60] => dual [61] => each [62] => else [63] => elseif [64] => empty [65] => enclosed [66] => escaped [67] => except [68] => exists [69] => exit [70] => explain [71] => false [72] => fetch [73] => first_value [74] => float [75] => float4 [76] => float8 [77] => for [78] => force [79] => foreign [80] => from [81] => fulltext [82] => function [83] => generated [84] => get [85] => grant [86] => group [87] => grouping [88] => groups [89] => having [90] => high_priority [91] => hour_microsecond [92] => hour_minute [93] => hour_second [94] => if [95] => ignore [96] => in [97] => index [98] => infile [99] => inner [100] => inout [101] => insensitive [102] => insert [103] => int [104] => int1 [105] => int2 [106] => int3 [107] => int4 [108] => int8 [109] => integer [110] => intersect [111] => interval [112] => into [113] => io_after_gtids [114] => io_before_gtids [115] => is [116] => iterate [117] => join [118] => json_table [119] => key [120] => keys [121] => kill [122] => lag [123] => last_value [124] => lateral [125] => lead [126] => leading [127] => leave [128] => left [129] => like [130] => limit [131] => linear [132] => lines [133] => load [134] => localtime [135] => localtimestamp [136] => lock [137] => long [138] => longblob [139] => longtext [140] => loop [141] => low_priority [142] => master_bind [143] => master_ssl_verify_server_cert [144] => match [145] => maxvalue [146] => mediumblob [147] => mediumint [148] => mediumtext [149] => middleint [150] => minute_microsecond [151] => minute_second [152] => mod [153] => modifies [154] => natural [155] => not [156] => no_write_to_binlog [157] => nth_value [158] => ntile [159] => null [160] => numeric [161] => of [162] => on [163] => optimize [164] => optimizer_costs [165] => option [166] => optionally [167] => or [168] => order [169] => out [170] => outer [171] => outfile [172] => over [173] => partition [174] => percent_rank [175] => persist [176] => persist_only [177] => precision [178] => primary [179] => procedure [180] => purge [181] => range [182] => rank [183] => read [184] => reads [185] => read_write [186] => real [187] => recursive [188] => references [189] => regexp [190] => release [191] => rename [192] => repeat [193] => replace [194] => require [195] => resignal [196] => restrict [197] => return [198] => revoke [199] => right [200] => rlike [201] => row [202] => rows [203] => row_number [204] => schema [205] => schemas [206] => second_microsecond [207] => select [208] => sensitive [209] => separator [210] => set [211] => show [212] => signal [213] => smallint [214] => spatial [215] => specific [216] => sql [217] => sqlexception [218] => sqlstate [219] => sqlwarning [220] => sql_big_result [221] => sql_calc_found_rows [222] => sql_small_result [223] => ssl [224] => starting [225] => stored [226] => straight_join [227] => system [228] => table [229] => terminated [230] => then [231] => tinyblob [232] => tinyint [233] => tinytext [234] => to [235] => trailing [236] => trigger [237] => true [238] => undo [239] => union [240] => unique [241] => unlock [242] => unsigned [243] => update [244] => usage [245] => use [246] => using [247] => utc_date [248] => utc_time [249] => utc_timestamp [250] => values [251] => varbinary [252] => varchar [253] => varcharacter [254] => varying [255] => virtual [256] => when [257] => where [258] => while [259] => window [260] => with [261] => write [262] => xor [263] => year_month [264] => zerofill ) ) [type] => -> [args] => Array ( [0] => Array ( [default] => ) ) ) [3] => Array ( [file] => /apps/hitra7/drupal7/includes/database/mysql/database.inc [line] => 349 [function] => __construct [class] => DatabaseConnection [object] => DatabaseConnection_mysql Object ( [target:protected] => [key:protected] => [logger:protected] => [transactionLayers:protected] => Array ( ) [driverClasses:protected] => Array ( ) [statementClass:protected] => DatabaseStatementBase [transactionSupport:protected] => 1 [transactionalDDLSupport:protected] => [temporaryNameIndex:protected] => 0 [connection:protected] => [connectionOptions:protected] => Array ( [driver] => mysql [database] => common_fe [username] => root [password] => asdf [host] => localhost [prefix] => Array ( [default] => ) ) [schema:protected] => [prefixes:protected] => Array ( [default] => ) [prefixSearch:protected] => Array ( [0] => { [1] => } ) [prefixReplace:protected] => Array ( [0] => [1] => ) [escapedNames:protected] => Array ( ) [escapedAliases:protected] => Array ( ) [unprefixedTablesMap:protected] => Array ( ) [needsCleanup:protected] => [reservedKeyWords:DatabaseConnection_mysql:private] => Array ( [0] => accessible [1] => add [2] => admin [3] => all [4] => alter [5] => analyze [6] => and [7] => as [8] => asc [9] => asensitive [10] => before [11] => between [12] => bigint [13] => binary [14] => blob [15] => both [16] => by [17] => call [18] => cascade [19] => case [20] => change [21] => char [22] => character [23] => check [24] => collate [25] => column [26] => condition [27] => constraint [28] => continue [29] => convert [30] => create [31] => cross [32] => cube [33] => cume_dist [34] => current_date [35] => current_time [36] => current_timestamp [37] => current_user [38] => cursor [39] => database [40] => databases [41] => day_hour [42] => day_microsecond [43] => day_minute [44] => day_second [45] => dec [46] => decimal [47] => declare [48] => default [49] => delayed [50] => delete [51] => dense_rank [52] => desc [53] => describe [54] => deterministic [55] => distinct [56] => distinctrow [57] => div [58] => double [59] => drop [60] => dual [61] => each [62] => else [63] => elseif [64] => empty [65] => enclosed [66] => escaped [67] => except [68] => exists [69] => exit [70] => explain [71] => false [72] => fetch [73] => first_value [74] => float [75] => float4 [76] => float8 [77] => for [78] => force [79] => foreign [80] => from [81] => fulltext [82] => function [83] => generated [84] => get [85] => grant [86] => group [87] => grouping [88] => groups [89] => having [90] => high_priority [91] => hour_microsecond [92] => hour_minute [93] => hour_second [94] => if [95] => ignore [96] => in [97] => index [98] => infile [99] => inner [100] => inout [101] => insensitive [102] => insert [103] => int [104] => int1 [105] => int2 [106] => int3 [107] => int4 [108] => int8 [109] => integer [110] => intersect [111] => interval [112] => into [113] => io_after_gtids [114] => io_before_gtids [115] => is [116] => iterate [117] => join [118] => json_table [119] => key [120] => keys [121] => kill [122] => lag [123] => last_value [124] => lateral [125] => lead [126] => leading [127] => leave [128] => left [129] => like [130] => limit [131] => linear [132] => lines [133] => load [134] => localtime [135] => localtimestamp [136] => lock [137] => long [138] => longblob [139] => longtext [140] => loop [141] => low_priority [142] => master_bind [143] => master_ssl_verify_server_cert [144] => match [145] => maxvalue [146] => mediumblob [147] => mediumint [148] => mediumtext [149] => middleint [150] => minute_microsecond [151] => minute_second [152] => mod [153] => modifies [154] => natural [155] => not [156] => no_write_to_binlog [157] => nth_value [158] => ntile [159] => null [160] => numeric [161] => of [162] => on [163] => optimize [164] => optimizer_costs [165] => option [166] => optionally [167] => or [168] => order [169] => out [170] => outer [171] => outfile [172] => over [173] => partition [174] => percent_rank [175] => persist [176] => persist_only [177] => precision [178] => primary [179] => procedure [180] => purge [181] => range [182] => rank [183] => read [184] => reads [185] => read_write [186] => real [187] => recursive [188] => references [189] => regexp [190] => release [191] => rename [192] => repeat [193] => replace [194] => require [195] => resignal [196] => restrict [197] => return [198] => revoke [199] => right [200] => rlike [201] => row [202] => rows [203] => row_number [204] => schema [205] => schemas [206] => second_microsecond [207] => select [208] => sensitive [209] => separator [210] => set [211] => show [212] => signal [213] => smallint [214] => spatial [215] => specific [216] => sql [217] => sqlexception [218] => sqlstate [219] => sqlwarning [220] => sql_big_result [221] => sql_calc_found_rows [222] => sql_small_result [223] => ssl [224] => starting [225] => stored [226] => straight_join [227] => system [228] => table [229] => terminated [230] => then [231] => tinyblob [232] => tinyint [233] => tinytext [234] => to [235] => trailing [236] => trigger [237] => true [238] => undo [239] => union [240] => unique [241] => unlock [242] => unsigned [243] => update [244] => usage [245] => use [246] => using [247] => utc_date [248] => utc_time [249] => utc_timestamp [250] => values [251] => varbinary [252] => varchar [253] => varcharacter [254] => varying [255] => virtual [256] => when [257] => where [258] => while [259] => window [260] => with [261] => write [262] => xor [263] => year_month [264] => zerofill ) ) [type] => -> [args] => Array ( [0] => mysql:host=localhost;port=3306;charset=utf8;dbname=common_fe [1] => root [2] => asdf [3] => Array ( [1000] => 1 [20] => 1 [17] => 1 [1013] => ) ) ) [4] => Array ( [file] => /apps/hitra7/drupal7/includes/database/database.inc [line] => 1796 [function] => __construct [class] => DatabaseConnection_mysql [object] => DatabaseConnection_mysql Object ( [target:protected] => [key:protected] => [logger:protected] => [transactionLayers:protected] => Array ( ) [driverClasses:protected] => Array ( ) [statementClass:protected] => DatabaseStatementBase [transactionSupport:protected] => 1 [transactionalDDLSupport:protected] => [temporaryNameIndex:protected] => 0 [connection:protected] => [connectionOptions:protected] => Array ( [driver] => mysql [database] => common_fe [username] => root [password] => asdf [host] => localhost [prefix] => Array ( [default] => ) ) [schema:protected] => [prefixes:protected] => Array ( [default] => ) [prefixSearch:protected] => Array ( [0] => { [1] => } ) [prefixReplace:protected] => Array ( [0] => [1] => ) [escapedNames:protected] => Array ( ) [escapedAliases:protected] => Array ( ) [unprefixedTablesMap:protected] => Array ( ) [needsCleanup:protected] => [reservedKeyWords:DatabaseConnection_mysql:private] => Array ( [0] => accessible [1] => add [2] => admin [3] => all [4] => alter [5] => analyze [6] => and [7] => as [8] => asc [9] => asensitive [10] => before [11] => between [12] => bigint [13] => binary [14] => blob [15] => both [16] => by [17] => call [18] => cascade [19] => case [20] => change [21] => char [22] => character [23] => check [24] => collate [25] => column [26] => condition [27] => constraint [28] => continue [29] => convert [30] => create [31] => cross [32] => cube [33] => cume_dist [34] => current_date [35] => current_time [36] => current_timestamp [37] => current_user [38] => cursor [39] => database [40] => databases [41] => day_hour [42] => day_microsecond [43] => day_minute [44] => day_second [45] => dec [46] => decimal [47] => declare [48] => default [49] => delayed [50] => delete [51] => dense_rank [52] => desc [53] => describe [54] => deterministic [55] => distinct [56] => distinctrow [57] => div [58] => double [59] => drop [60] => dual [61] => each [62] => else [63] => elseif [64] => empty [65] => enclosed [66] => escaped [67] => except [68] => exists [69] => exit [70] => explain [71] => false [72] => fetch [73] => first_value [74] => float [75] => float4 [76] => float8 [77] => for [78] => force [79] => foreign [80] => from [81] => fulltext [82] => function [83] => generated [84] => get [85] => grant [86] => group [87] => grouping [88] => groups [89] => having [90] => high_priority [91] => hour_microsecond [92] => hour_minute [93] => hour_second [94] => if [95] => ignore [96] => in [97] => index [98] => infile [99] => inner [100] => inout [101] => insensitive [102] => insert [103] => int [104] => int1 [105] => int2 [106] => int3 [107] => int4 [108] => int8 [109] => integer [110] => intersect [111] => interval [112] => into [113] => io_after_gtids [114] => io_before_gtids [115] => is [116] => iterate [117] => join [118] => json_table [119] => key [120] => keys [121] => kill [122] => lag [123] => last_value [124] => lateral [125] => lead [126] => leading [127] => leave [128] => left [129] => like [130] => limit [131] => linear [132] => lines [133] => load [134] => localtime [135] => localtimestamp [136] => lock [137] => long [138] => longblob [139] => longtext [140] => loop [141] => low_priority [142] => master_bind [143] => master_ssl_verify_server_cert [144] => match [145] => maxvalue [146] => mediumblob [147] => mediumint [148] => mediumtext [149] => middleint [150] => minute_microsecond [151] => minute_second [152] => mod [153] => modifies [154] => natural [155] => not [156] => no_write_to_binlog [157] => nth_value [158] => ntile [159] => null [160] => numeric [161] => of [162] => on [163] => optimize [164] => optimizer_costs [165] => option [166] => optionally [167] => or [168] => order [169] => out [170] => outer [171] => outfile [172] => over [173] => partition [174] => percent_rank [175] => persist [176] => persist_only [177] => precision [178] => primary [179] => procedure [180] => purge [181] => range [182] => rank [183] => read [184] => reads [185] => read_write [186] => real [187] => recursive [188] => references [189] => regexp [190] => release [191] => rename [192] => repeat [193] => replace [194] => require [195] => resignal [196] => restrict [197] => return [198] => revoke [199] => right [200] => rlike [201] => row [202] => rows [203] => row_number [204] => schema [205] => schemas [206] => second_microsecond [207] => select [208] => sensitive [209] => separator [210] => set [211] => show [212] => signal [213] => smallint [214] => spatial [215] => specific [216] => sql [217] => sqlexception [218] => sqlstate [219] => sqlwarning [220] => sql_big_result [221] => sql_calc_found_rows [222] => sql_small_result [223] => ssl [224] => starting [225] => stored [226] => straight_join [227] => system [228] => table [229] => terminated [230] => then [231] => tinyblob [232] => tinyint [233] => tinytext [234] => to [235] => trailing [236] => trigger [237] => true [238] => undo [239] => union [240] => unique [241] => unlock [242] => unsigned [243] => update [244] => usage [245] => use [246] => using [247] => utc_date [248] => utc_time [249] => utc_timestamp [250] => values [251] => varbinary [252] => varchar [253] => varcharacter [254] => varying [255] => virtual [256] => when [257] => where [258] => while [259] => window [260] => with [261] => write [262] => xor [263] => year_month [264] => zerofill ) ) [type] => -> [args] => Array ( [0] => Array ( [driver] => mysql [database] => common_fe [username] => root [password] => asdf [host] => localhost [prefix] => Array ( [default] => ) [pdo] => Array ( [1000] => 1 [20] => 1 [17] => 1 [1013] => ) ) ) ) [5] => Array ( [file] => /apps/hitra7/drupal7/includes/database/database.inc [line] => 1582 [function] => openConnection [class] => Database [type] => :: [args] => Array ( [0] => common_fe [1] => default ) ) [6] => Array ( [file] => /apps/hitra7/drupal7/includes/database/database.inc [line] => 2467 [function] => getConnection [class] => Database [type] => :: [args] => Array ( [0] => default ) ) [7] => Array ( [file] => /apps/nobleprog-website/includes/functions/new-modules-general-functions.php [line] => 31 [function] => db_query [args] => Array ( [0] => SELECT * FROM fe_regions_translation frt LEFT JOIN fe_regions fr ON frt.region_id = fr.region_id WHERE frt.url_path_mapper = :region_path_mapper AND frt.language = :language AND fr.sales_area IN (:sales_areas) AND frt.region_publish_status = 1 [1] => Array ( [:region_path_mapper] => guayaquil [:language] => es [:sales_areas] => Array ( [0] => ec_ecuador ) ) ) ) [8] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 175 [function] => np_db_query [args] => Array ( [0] => common_fe [1] => db_query [2] => SELECT * FROM fe_regions_translation frt LEFT JOIN fe_regions fr ON frt.region_id = fr.region_id WHERE frt.url_path_mapper = :region_path_mapper AND frt.language = :language AND fr.sales_area IN (:sales_areas) AND frt.region_publish_status = 1 [3] => Array ( [:region_path_mapper] => guayaquil [:language] => es [:sales_areas] => Array ( [0] => ec_ecuador ) ) ) ) [9] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 58 [function] => find_region_by_url_path_mapper [args] => Array ( [0] => guayaquil [1] => es ) ) [10] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => apache-arrow [1] => guayaquil ) ) [11] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /apache-arrow/cursos/guayaquil ) ) [12] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [13] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [14] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/apache-arrow/cursos/guayaquil Undefined array key "mysql_identifier_quote_character" /apps/nobleprog-website/includes/functions/new-modules-general-functions.php:82 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/new-modules-general-functions.php [line] => 82 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "mysql_identifier_quote_character" [2] => /apps/nobleprog-website/includes/functions/new-modules-general-functions.php [3] => 82 ) ) [1] => Array ( [file] => /apps/hitra7/drupal7/includes/database/mysql/database.inc [line] => 397 [function] => variable_get [args] => Array ( [0] => mysql_identifier_quote_character [1] => ` ) ) [2] => Array ( [file] => /apps/hitra7/drupal7/includes/database/database.inc [line] => 329 [function] => setPrefix [class] => DatabaseConnection_mysql [object] => DatabaseConnection_mysql Object ( [target:protected] => [key:protected] => [logger:protected] => [transactionLayers:protected] => Array ( ) [driverClasses:protected] => Array ( ) [statementClass:protected] => DatabaseStatementBase [transactionSupport:protected] => 1 [transactionalDDLSupport:protected] => [temporaryNameIndex:protected] => 0 [connection:protected] => [connectionOptions:protected] => Array ( [driver] => mysql [database] => hitrahr [username] => root [password] => asdf [host] => localhost [prefix] => Array ( [default] => ) ) [schema:protected] => [prefixes:protected] => Array ( [default] => ) [prefixSearch:protected] => Array ( [0] => { [1] => } ) [prefixReplace:protected] => Array ( [0] => [1] => ) [escapedNames:protected] => Array ( ) [escapedAliases:protected] => Array ( ) [unprefixedTablesMap:protected] => Array ( ) [needsCleanup:protected] => [reservedKeyWords:DatabaseConnection_mysql:private] => Array ( [0] => accessible [1] => add [2] => admin [3] => all [4] => alter [5] => analyze [6] => and [7] => as [8] => asc [9] => asensitive [10] => before [11] => between [12] => bigint [13] => binary [14] => blob [15] => both [16] => by [17] => call [18] => cascade [19] => case [20] => change [21] => char [22] => character [23] => check [24] => collate [25] => column [26] => condition [27] => constraint [28] => continue [29] => convert [30] => create [31] => cross [32] => cube [33] => cume_dist [34] => current_date [35] => current_time [36] => current_timestamp [37] => current_user [38] => cursor [39] => database [40] => databases [41] => day_hour [42] => day_microsecond [43] => day_minute [44] => day_second [45] => dec [46] => decimal [47] => declare [48] => default [49] => delayed [50] => delete [51] => dense_rank [52] => desc [53] => describe [54] => deterministic [55] => distinct [56] => distinctrow [57] => div [58] => double [59] => drop [60] => dual [61] => each [62] => else [63] => elseif [64] => empty [65] => enclosed [66] => escaped [67] => except [68] => exists [69] => exit [70] => explain [71] => false [72] => fetch [73] => first_value [74] => float [75] => float4 [76] => float8 [77] => for [78] => force [79] => foreign [80] => from [81] => fulltext [82] => function [83] => generated [84] => get [85] => grant [86] => group [87] => grouping [88] => groups [89] => having [90] => high_priority [91] => hour_microsecond [92] => hour_minute [93] => hour_second [94] => if [95] => ignore [96] => in [97] => index [98] => infile [99] => inner [100] => inout [101] => insensitive [102] => insert [103] => int [104] => int1 [105] => int2 [106] => int3 [107] => int4 [108] => int8 [109] => integer [110] => intersect [111] => interval [112] => into [113] => io_after_gtids [114] => io_before_gtids [115] => is [116] => iterate [117] => join [118] => json_table [119] => key [120] => keys [121] => kill [122] => lag [123] => last_value [124] => lateral [125] => lead [126] => leading [127] => leave [128] => left [129] => like [130] => limit [131] => linear [132] => lines [133] => load [134] => localtime [135] => localtimestamp [136] => lock [137] => long [138] => longblob [139] => longtext [140] => loop [141] => low_priority [142] => master_bind [143] => master_ssl_verify_server_cert [144] => match [145] => maxvalue [146] => mediumblob [147] => mediumint [148] => mediumtext [149] => middleint [150] => minute_microsecond [151] => minute_second [152] => mod [153] => modifies [154] => natural [155] => not [156] => no_write_to_binlog [157] => nth_value [158] => ntile [159] => null [160] => numeric [161] => of [162] => on [163] => optimize [164] => optimizer_costs [165] => option [166] => optionally [167] => or [168] => order [169] => out [170] => outer [171] => outfile [172] => over [173] => partition [174] => percent_rank [175] => persist [176] => persist_only [177] => precision [178] => primary [179] => procedure [180] => purge [181] => range [182] => rank [183] => read [184] => reads [185] => read_write [186] => real [187] => recursive [188] => references [189] => regexp [190] => release [191] => rename [192] => repeat [193] => replace [194] => require [195] => resignal [196] => restrict [197] => return [198] => revoke [199] => right [200] => rlike [201] => row [202] => rows [203] => row_number [204] => schema [205] => schemas [206] => second_microsecond [207] => select [208] => sensitive [209] => separator [210] => set [211] => show [212] => signal [213] => smallint [214] => spatial [215] => specific [216] => sql [217] => sqlexception [218] => sqlstate [219] => sqlwarning [220] => sql_big_result [221] => sql_calc_found_rows [222] => sql_small_result [223] => ssl [224] => starting [225] => stored [226] => straight_join [227] => system [228] => table [229] => terminated [230] => then [231] => tinyblob [232] => tinyint [233] => tinytext [234] => to [235] => trailing [236] => trigger [237] => true [238] => undo [239] => union [240] => unique [241] => unlock [242] => unsigned [243] => update [244] => usage [245] => use [246] => using [247] => utc_date [248] => utc_time [249] => utc_timestamp [250] => values [251] => varbinary [252] => varchar [253] => varcharacter [254] => varying [255] => virtual [256] => when [257] => where [258] => while [259] => window [260] => with [261] => write [262] => xor [263] => year_month [264] => zerofill ) ) [type] => -> [args] => Array ( [0] => Array ( [default] => ) ) ) [3] => Array ( [file] => /apps/hitra7/drupal7/includes/database/mysql/database.inc [line] => 349 [function] => __construct [class] => DatabaseConnection [object] => DatabaseConnection_mysql Object ( [target:protected] => [key:protected] => [logger:protected] => [transactionLayers:protected] => Array ( ) [driverClasses:protected] => Array ( ) [statementClass:protected] => DatabaseStatementBase [transactionSupport:protected] => 1 [transactionalDDLSupport:protected] => [temporaryNameIndex:protected] => 0 [connection:protected] => [connectionOptions:protected] => Array ( [driver] => mysql [database] => hitrahr [username] => root [password] => asdf [host] => localhost [prefix] => Array ( [default] => ) ) [schema:protected] => [prefixes:protected] => Array ( [default] => ) [prefixSearch:protected] => Array ( [0] => { [1] => } ) [prefixReplace:protected] => Array ( [0] => [1] => ) [escapedNames:protected] => Array ( ) [escapedAliases:protected] => Array ( ) [unprefixedTablesMap:protected] => Array ( ) [needsCleanup:protected] => [reservedKeyWords:DatabaseConnection_mysql:private] => Array ( [0] => accessible [1] => add [2] => admin [3] => all [4] => alter [5] => analyze [6] => and [7] => as [8] => asc [9] => asensitive [10] => before [11] => between [12] => bigint [13] => binary [14] => blob [15] => both [16] => by [17] => call [18] => cascade [19] => case [20] => change [21] => char [22] => character [23] => check [24] => collate [25] => column [26] => condition [27] => constraint [28] => continue [29] => convert [30] => create [31] => cross [32] => cube [33] => cume_dist [34] => current_date [35] => current_time [36] => current_timestamp [37] => current_user [38] => cursor [39] => database [40] => databases [41] => day_hour [42] => day_microsecond [43] => day_minute [44] => day_second [45] => dec [46] => decimal [47] => declare [48] => default [49] => delayed [50] => delete [51] => dense_rank [52] => desc [53] => describe [54] => deterministic [55] => distinct [56] => distinctrow [57] => div [58] => double [59] => drop [60] => dual [61] => each [62] => else [63] => elseif [64] => empty [65] => enclosed [66] => escaped [67] => except [68] => exists [69] => exit [70] => explain [71] => false [72] => fetch [73] => first_value [74] => float [75] => float4 [76] => float8 [77] => for [78] => force [79] => foreign [80] => from [81] => fulltext [82] => function [83] => generated [84] => get [85] => grant [86] => group [87] => grouping [88] => groups [89] => having [90] => high_priority [91] => hour_microsecond [92] => hour_minute [93] => hour_second [94] => if [95] => ignore [96] => in [97] => index [98] => infile [99] => inner [100] => inout [101] => insensitive [102] => insert [103] => int [104] => int1 [105] => int2 [106] => int3 [107] => int4 [108] => int8 [109] => integer [110] => intersect [111] => interval [112] => into [113] => io_after_gtids [114] => io_before_gtids [115] => is [116] => iterate [117] => join [118] => json_table [119] => key [120] => keys [121] => kill [122] => lag [123] => last_value [124] => lateral [125] => lead [126] => leading [127] => leave [128] => left [129] => like [130] => limit [131] => linear [132] => lines [133] => load [134] => localtime [135] => localtimestamp [136] => lock [137] => long [138] => longblob [139] => longtext [140] => loop [141] => low_priority [142] => master_bind [143] => master_ssl_verify_server_cert [144] => match [145] => maxvalue [146] => mediumblob [147] => mediumint [148] => mediumtext [149] => middleint [150] => minute_microsecond [151] => minute_second [152] => mod [153] => modifies [154] => natural [155] => not [156] => no_write_to_binlog [157] => nth_value [158] => ntile [159] => null [160] => numeric [161] => of [162] => on [163] => optimize [164] => optimizer_costs [165] => option [166] => optionally [167] => or [168] => order [169] => out [170] => outer [171] => outfile [172] => over [173] => partition [174] => percent_rank [175] => persist [176] => persist_only [177] => precision [178] => primary [179] => procedure [180] => purge [181] => range [182] => rank [183] => read [184] => reads [185] => read_write [186] => real [187] => recursive [188] => references [189] => regexp [190] => release [191] => rename [192] => repeat [193] => replace [194] => require [195] => resignal [196] => restrict [197] => return [198] => revoke [199] => right [200] => rlike [201] => row [202] => rows [203] => row_number [204] => schema [205] => schemas [206] => second_microsecond [207] => select [208] => sensitive [209] => separator [210] => set [211] => show [212] => signal [213] => smallint [214] => spatial [215] => specific [216] => sql [217] => sqlexception [218] => sqlstate [219] => sqlwarning [220] => sql_big_result [221] => sql_calc_found_rows [222] => sql_small_result [223] => ssl [224] => starting [225] => stored [226] => straight_join [227] => system [228] => table [229] => terminated [230] => then [231] => tinyblob [232] => tinyint [233] => tinytext [234] => to [235] => trailing [236] => trigger [237] => true [238] => undo [239] => union [240] => unique [241] => unlock [242] => unsigned [243] => update [244] => usage [245] => use [246] => using [247] => utc_date [248] => utc_time [249] => utc_timestamp [250] => values [251] => varbinary [252] => varchar [253] => varcharacter [254] => varying [255] => virtual [256] => when [257] => where [258] => while [259] => window [260] => with [261] => write [262] => xor [263] => year_month [264] => zerofill ) ) [type] => -> [args] => Array ( [0] => mysql:host=localhost;port=3306;charset=utf8;dbname=hitrahr [1] => root [2] => asdf [3] => Array ( [1000] => 1 [20] => 1 [17] => 1 [1013] => ) ) ) [4] => Array ( [file] => /apps/hitra7/drupal7/includes/database/database.inc [line] => 1796 [function] => __construct [class] => DatabaseConnection_mysql [object] => DatabaseConnection_mysql Object ( [target:protected] => [key:protected] => [logger:protected] => [transactionLayers:protected] => Array ( ) [driverClasses:protected] => Array ( ) [statementClass:protected] => DatabaseStatementBase [transactionSupport:protected] => 1 [transactionalDDLSupport:protected] => [temporaryNameIndex:protected] => 0 [connection:protected] => [connectionOptions:protected] => Array ( [driver] => mysql [database] => hitrahr [username] => root [password] => asdf [host] => localhost [prefix] => Array ( [default] => ) ) [schema:protected] => [prefixes:protected] => Array ( [default] => ) [prefixSearch:protected] => Array ( [0] => { [1] => } ) [prefixReplace:protected] => Array ( [0] => [1] => ) [escapedNames:protected] => Array ( ) [escapedAliases:protected] => Array ( ) [unprefixedTablesMap:protected] => Array ( ) [needsCleanup:protected] => [reservedKeyWords:DatabaseConnection_mysql:private] => Array ( [0] => accessible [1] => add [2] => admin [3] => all [4] => alter [5] => analyze [6] => and [7] => as [8] => asc [9] => asensitive [10] => before [11] => between [12] => bigint [13] => binary [14] => blob [15] => both [16] => by [17] => call [18] => cascade [19] => case [20] => change [21] => char [22] => character [23] => check [24] => collate [25] => column [26] => condition [27] => constraint [28] => continue [29] => convert [30] => create [31] => cross [32] => cube [33] => cume_dist [34] => current_date [35] => current_time [36] => current_timestamp [37] => current_user [38] => cursor [39] => database [40] => databases [41] => day_hour [42] => day_microsecond [43] => day_minute [44] => day_second [45] => dec [46] => decimal [47] => declare [48] => default [49] => delayed [50] => delete [51] => dense_rank [52] => desc [53] => describe [54] => deterministic [55] => distinct [56] => distinctrow [57] => div [58] => double [59] => drop [60] => dual [61] => each [62] => else [63] => elseif [64] => empty [65] => enclosed [66] => escaped [67] => except [68] => exists [69] => exit [70] => explain [71] => false [72] => fetch [73] => first_value [74] => float [75] => float4 [76] => float8 [77] => for [78] => force [79] => foreign [80] => from [81] => fulltext [82] => function [83] => generated [84] => get [85] => grant [86] => group [87] => grouping [88] => groups [89] => having [90] => high_priority [91] => hour_microsecond [92] => hour_minute [93] => hour_second [94] => if [95] => ignore [96] => in 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En línea o en el sitio, los cursos de capacitación en vivo de Apache Arrow dirigidos por un instructor demuestran a través de la práctica interactiva cómo usar Apache Arrow para procesar datos de fuentes de datos dispares. La capacitación de Apache Arrow está disponible como "capacitación en vivo en línea" o "capacitación en vivo en el sitio". La capacitación en vivo en línea (también conocida como "capacitación remota en vivo") se lleva a cabo a través de un escritorio remoto interactivo La capacitación en vivo in situ se puede llevar a cabo localmente en las instalaciones del cliente en o en los centros de capacitación corporativa de NobleProg en . NobleProg -- Su proveedor local de capacitación

[language] => [url_path_mapper] => apache-arrow [english_name] => Apache Arrow [original_description] => Online or onsite, instructor-led live Apache Arrow training courses demonstrate through interactive hands-on practice how to use Apache Arrow to process data from disparate data sources.. Apache Arrow training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Onsite live training can be carried out locally on customer premises in or in NobleProg corporate training centers in . NobleProg -- Your Local Training Provider [consulting_options] => available ) [1] => Apache Arrow [2] => Ecuador [3] => stdClass Object ( [language] => es [name] => Spanish [native] => Español [prefix] => es [default_language] => es [language_url] => [secondary_language] => [language_switcher_links] => Array ( [es] => /apache-arrow/cursos/guayaquil [en] => /en/apache-arrow/training/guayaquil ) [multi_lingual] => 1 ) ) ) [2] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => apache-arrow [1] => guayaquil ) ) [3] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /apache-arrow/cursos/guayaquil ) ) [4] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [5] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [6] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/apache-arrow/cursos/guayaquil Undefined array key "es" /apps/hitra7/drupal7/sites/all/modules/_custom/frontend/np_outline/np_outline.module:66 Array ( [0] => Array ( [file] => /apps/hitra7/drupal7/sites/all/modules/_custom/frontend/np_outline/np_outline.module [line] => 66 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "es" [2] => /apps/hitra7/drupal7/sites/all/modules/_custom/frontend/np_outline/np_outline.module [3] => 66 ) ) [1] => Array ( [file] => /apps/hitra7/drupal7/sites/all/modules/_custom/frontend/np_outline/np_outline.module [line] => 25 [function] => np_outline_title [args] => Array ( [0] => arrow [1] => ) ) [2] => Array ( [file] => /apps/hitra7/drupal7/sites/all/modules/_custom/common/hrquery/frontend/denorm.logic.php [line] => 46 [function] => np_outline_get_value [args] => Array ( [0] => Array ( [en] => stdClass Object ( [course_code] => arrow [hr_nid] => 284282 [title] => Apache Arrow for Data Analysis across Disparate Data Sources [requirements] => [overview] =>

Apache Arrow is an open-source in-memory data processing framework. It is often used together with other data science tools for accessing disparate data stores for analysis. It integrates well with other technologies such as GPU databases, machine learning libraries and tools, execution engines, and data visualization frameworks.

In this onsite instructor-led, live training, participants will learn how to integrate Apache Arrow with various Data Science frameworks to access data from disparate data sources.

By the end of this training, participants will be able to:

Audience

Format of the Course

Note

[category_overview] => [outline] =>

Introduction

Installing and Configuring Apache Arrow

Overview of Apache Arrow Features and Architecture

Exploring Data with Pandas and Apache Arrow

Exploring Data with Spark and Apache Arrow

Exploring Data with R and Apache Arrow

Exploring Data with MapD and Apache Arrow

Other Data Analysis Integrations

Troubleshooting

Summary and Conclusion

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Apache Arrow es un marco de procesamiento de datos en memoria de código abierto. A menudo se usa junto con otras herramientas de ciencia de datos para acceder a almacenes de datos dispares para su análisis. Se integra bien con otras tecnologías, como bases de datos de GPU , bibliotecas y herramientas de aprendizaje automático, motores de ejecución y marcos de visualización de datos.

En esta capacitación en vivo dirigida por un instructor en el sitio, los participantes aprenderán cómo integrar Apache Arrow con varios marcos de Data Science para acceder a datos de fuentes de datos dispares.

Al final de esta capacitación, los participantes podrán:

Audiencia

Formato del curso

Nota

[category_overview] => [outline] => [language] => es [duration] => 14 [status] => published [changed] => 1700037432 [source_title] => Apache Arrow for Data Analysis across Disparate Data Sources [source_language] => en [cert_code] => [weight] => -976 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => 1 [field_prerequisits] => 1 [field_overview_in_category] => ) ) ) ) ) [3] => Array ( [file] => /apps/hitra7/drupal7/sites/all/modules/_custom/common/hrquery/frontend/denorm.logic.php [line] => 54 [function] => hrquery_get_outline_by_cc_langs [args] => Array ( [0] => arrow [1] => Array ( [0] => en [1] => es ) ) ) [4] => Array ( [file] => /apps/hitra7/drupal7/sites/all/modules/_custom/common/hrquery/frontend/denorm.logic.php [line] => 63 [function] => _hrquery_get_outline_by_course_code [args] => Array ( [0] => arrow [1] => Array ( [0] => en [1] => es ) ) ) [5] => Array ( [file] => /apps/hitra7/drupal7/sites/all/modules/_custom/common/hrquery/frontend/denorm.logic.php [line] => 74 [function] => hrquery_get_outline_by_course_code [args] => Array ( [0] => Array ( [0] => arrow ) [1] => Array ( [0] => en [1] => es ) ) ) [6] => Array ( [file] => /apps/hitra7/drupal7/sites/all/modules/_custom/common/hrquery/frontend/denorm.logic.php [line] => 118 [function] => hrquery_get_outlines_by_course_codes [args] => Array ( [0] => Array ( [0] => arrow ) ) ) [7] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-functions.php [line] => 25 [function] => hrquery_denorm_get_outlines_with_plain_text_overview_by_course_codes [args] => Array ( [0] => Array ( [0] => arrow ) ) ) [8] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 313 [function] => term_cat_page_render_course_in_category_v2 [args] => Array ( [0] => 2983 [1] => 1 ) ) [9] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 91 [function] => category_get_outlines_in_category [args] => Array ( [0] => Array ( [tid] => 2983 [url_alias] => cursos-apache-arrow [name] => Apache Arrow [description] =>

En línea o en el sitio, los cursos de capacitación en vivo de Apache Arrow dirigidos por un instructor demuestran a través de la práctica interactiva cómo usar Apache Arrow para procesar datos de fuentes de datos dispares. La capacitación de Apache Arrow está disponible como "capacitación en vivo en línea" o "capacitación en vivo en el sitio". La capacitación en vivo en línea (también conocida como "capacitación remota en vivo") se lleva a cabo a través de un escritorio remoto interactivo La capacitación en vivo in situ se puede llevar a cabo localmente en las instalaciones del cliente en o en los centros de capacitación corporativa de NobleProg en . NobleProg -- Su proveedor local de capacitación

[language] => [url_path_mapper] => apache-arrow [english_name] => Apache Arrow [original_description] => Online or onsite, instructor-led live Apache Arrow training courses demonstrate through interactive hands-on practice how to use Apache Arrow to process data from disparate data sources.. Apache Arrow training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Onsite live training can be carried out locally on customer premises in or in NobleProg corporate training centers in . NobleProg -- Your Local Training Provider [consulting_options] => available ) ) ) [10] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => apache-arrow [1] => guayaquil ) ) [11] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /apache-arrow/cursos/guayaquil ) ) [12] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [13] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [14] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/apache-arrow/cursos/guayaquil Undefined array key "nobleprog_site_production_url" /apps/nobleprog-website/includes/functions/new-modules-general-functions.php:82 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/new-modules-general-functions.php [line] => 82 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "nobleprog_site_production_url" [2] => /apps/nobleprog-website/includes/functions/new-modules-general-functions.php [3] => 82 ) ) [1] => Array ( [file] => /apps/hitra7/drupal7/sites/all/modules/_custom/frontend/islc7/islc7.module [line] => 131 [function] => variable_get [args] => Array ( [0] => nobleprog_site_production_url ) ) [2] => Array ( [file] => /apps/hitra7/drupal7/sites/all/modules/_custom/frontend/islc7/islc7.module [line] => 94 [function] => islc_get_current_site [args] => Array ( ) ) [3] => Array ( [file] => /apps/hitra7/drupal7/sites/all/modules/_custom/frontend/islc7/islc7_block.inc [line] => 34 [function] => islc_get_site_list [args] => Array ( ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 94 [function] => islc7_sites_links_array_v3 [args] => Array ( ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => apache-arrow [1] => guayaquil ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /apache-arrow/cursos/guayaquil ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/apache-arrow/cursos/guayaquil Undefined array key "devel_domain" /apps/nobleprog-website/includes/functions/new-modules-general-functions.php:82 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/new-modules-general-functions.php [line] => 82 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "devel_domain" [2] => /apps/nobleprog-website/includes/functions/new-modules-general-functions.php [3] => 82 ) ) [1] => Array ( [file] => /apps/hitra7/drupal7/sites/all/modules/_custom/frontend/islc7/islc7.module [line] => 99 [function] => variable_get [args] => Array ( [0] => devel_domain [1] => ) ) [2] => Array ( [file] => /apps/hitra7/drupal7/sites/all/modules/_custom/frontend/islc7/islc7_block.inc [line] => 34 [function] => islc_get_site_list [args] => Array ( ) ) [3] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 94 [function] => islc7_sites_links_array_v3 [args] => Array ( ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => apache-arrow [1] => guayaquil ) ) [5] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /apache-arrow/cursos/guayaquil ) ) [6] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [7] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [8] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/apache-arrow/cursos/guayaquil Undefined array key "nobleprog_site_production_url" /apps/nobleprog-website/includes/functions/new-modules-general-functions.php:82 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/new-modules-general-functions.php [line] => 82 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "nobleprog_site_production_url" [2] => /apps/nobleprog-website/includes/functions/new-modules-general-functions.php [3] => 82 ) ) [1] => Array ( [file] => /apps/hitra7/drupal7/sites/all/modules/_custom/frontend/islc7/islc7.module [line] => 131 [function] => variable_get [args] => Array ( [0] => nobleprog_site_production_url ) ) [2] => Array ( [file] => /apps/hitra7/drupal7/sites/all/modules/_custom/frontend/islc7/islc7_block.inc [line] => 44 [function] => islc_get_current_site [args] => Array ( ) ) [3] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 94 [function] => islc7_sites_links_array_v3 [args] => Array ( ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => apache-arrow [1] => guayaquil ) ) [5] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /apache-arrow/cursos/guayaquil ) ) [6] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [7] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [8] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/apache-arrow/cursos/guayaquil Undefined array key "sdp" /apps/nobleprog-website/includes/functions/course-prices.php:281 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 281 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "sdp" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 281 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 5437 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => arrow [venue_id] => ec_15661446 [vfdc] => 175.00 [vadc] => 60.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => arrow ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [arrow] => stdClass Object ( [course_code] => arrow [hr_nid] => 284282 [title] => Apache Arrow for Data Analysis across Disparate Data Sources [requirements] =>
  • A basic undersanding of SQL
  • Familiarity with Python or R
  • Some familiarity with Apache Spark
[overview] => Apache Arrow es un marco de procesamiento de datos en memoria de código abierto. A menudo se usa junto con otras herramientas de ciencia de datos para acceder a almacenes de datos dispares para su análisis. Se integra bien con otras tecnologías, como bases de datos de GPU , bibliotecas y herramientas de aprendizaje automático, motores de ejecución y marcos de visualización de datos. En esta capacitación en vivo dirigida por un instructor en el sitio, los participantes aprenderán cómo integrar Apache Arrow con varios marcos de Data Science para acceder a datos de fuentes de datos dispares. Al final de esta capacitación, los participantes podrán:
  • Instale y configure Apache Arrow en un entorno agrupado distribuido
  • Use Apache Arrow para acceder a datos de fuentes de datos dispares
  • Use Apache Arrow para evitar la necesidad de construir y mantener tuberías complejas de ETL
  • Analice datos a través de fuentes de datos dispares sin tener que consolidarlos en un repositorio centralizado.
Audiencia
  • Científicos de datos
  • Ingenieros de datos
Formato del curso
  • Conferencia parcial, discusión parcial, ejercicios y práctica práctica.
Nota
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => [outline] =>

Introduction

  • Apache Arrow vs Parquet

Installing and Configuring Apache Arrow

Overview of Apache Arrow Features and Architecture

Exploring Data with Pandas and Apache Arrow

Exploring Data with Spark and Apache Arrow

Exploring Data with R and Apache Arrow

Exploring Data with MapD and Apache Arrow

Other Data Analysis Integrations

  • PySpark, Parquet files on S3, and Oracle tables and Elasticsearch indices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037432 [source_title] => Apache Arrow for Data Analysis across Disparate Data Sources [source_language] => en [cert_code] => [weight] => -976 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => arrow ) ) [1] => Array ( [0] => ec_4966 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => arrow [hr_nid] => 284282 [title] => Apache Arrow for Data Analysis across Disparate Data Sources [requirements] =>
  • A basic undersanding of SQL
  • Familiarity with Python or R
  • Some familiarity with Apache Spark
[overview] => Apache Arrow es un marco de procesamiento de datos en memoria de código abierto. A menudo se usa junto con otras herramientas de ciencia de datos para acceder a almacenes de datos dispares para su análisis. Se integra bien con otras tecnologías, como bases de datos de GPU , bibliotecas y herramientas de aprendizaje automático, motores de ejecución y marcos de visualización de datos. En esta capacitación en vivo dirigida por un instructor en el sitio, los participantes aprenderán cómo integrar Apache Arrow con varios marcos de Data Science para acceder a datos de fuentes de datos dispares. Al final de esta capacitación, los participantes podrán:
  • Instale y configure Apache Arrow en un entorno agrupado distribuido
  • Use Apache Arrow para acceder a datos de fuentes de datos dispares
  • Use Apache Arrow para evitar la necesidad de construir y mantener tuberías complejas de ETL
  • Analice datos a través de fuentes de datos dispares sin tener que consolidarlos en un repositorio centralizado.
Audiencia
  • Científicos de datos
  • Ingenieros de datos
Formato del curso
  • Conferencia parcial, discusión parcial, ejercicios y práctica práctica.
Nota
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => [outline] =>

Introduction

  • Apache Arrow vs Parquet

Installing and Configuring Apache Arrow

Overview of Apache Arrow Features and Architecture

Exploring Data with Pandas and Apache Arrow

Exploring Data with Spark and Apache Arrow

Exploring Data with R and Apache Arrow

Exploring Data with MapD and Apache Arrow

Other Data Analysis Integrations

  • PySpark, Parquet files on S3, and Oracle tables and Elasticsearch indices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037432 [source_title] => Apache Arrow for Data Analysis across Disparate Data Sources [source_language] => en [cert_code] => [weight] => -976 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => arrow ) ) [2] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => apache-arrow [1] => guayaquil ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /apache-arrow/cursos/guayaquil ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/apache-arrow/cursos/guayaquil Undefined array key "nobleprog_default_trainer_journey" /apps/nobleprog-website/includes/functions/course-prices.php:286 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 286 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "nobleprog_default_trainer_journey" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 286 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 5437 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => arrow [venue_id] => ec_15661446 [vfdc] => 175.00 [vadc] => 60.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => arrow ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [arrow] => stdClass Object ( [course_code] => arrow [hr_nid] => 284282 [title] => Apache Arrow for Data Analysis across Disparate Data Sources [requirements] =>
  • A basic undersanding of SQL
  • Familiarity with Python or R
  • Some familiarity with Apache Spark
[overview] => Apache Arrow es un marco de procesamiento de datos en memoria de código abierto. A menudo se usa junto con otras herramientas de ciencia de datos para acceder a almacenes de datos dispares para su análisis. Se integra bien con otras tecnologías, como bases de datos de GPU , bibliotecas y herramientas de aprendizaje automático, motores de ejecución y marcos de visualización de datos. En esta capacitación en vivo dirigida por un instructor en el sitio, los participantes aprenderán cómo integrar Apache Arrow con varios marcos de Data Science para acceder a datos de fuentes de datos dispares. Al final de esta capacitación, los participantes podrán:
  • Instale y configure Apache Arrow en un entorno agrupado distribuido
  • Use Apache Arrow para acceder a datos de fuentes de datos dispares
  • Use Apache Arrow para evitar la necesidad de construir y mantener tuberías complejas de ETL
  • Analice datos a través de fuentes de datos dispares sin tener que consolidarlos en un repositorio centralizado.
Audiencia
  • Científicos de datos
  • Ingenieros de datos
Formato del curso
  • Conferencia parcial, discusión parcial, ejercicios y práctica práctica.
Nota
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => [outline] =>

Introduction

  • Apache Arrow vs Parquet

Installing and Configuring Apache Arrow

Overview of Apache Arrow Features and Architecture

Exploring Data with Pandas and Apache Arrow

Exploring Data with Spark and Apache Arrow

Exploring Data with R and Apache Arrow

Exploring Data with MapD and Apache Arrow

Other Data Analysis Integrations

  • PySpark, Parquet files on S3, and Oracle tables and Elasticsearch indices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037432 [source_title] => Apache Arrow for Data Analysis across Disparate Data Sources [source_language] => en [cert_code] => [weight] => -976 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => arrow ) ) [1] => Array ( [0] => ec_4966 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => arrow [hr_nid] => 284282 [title] => Apache Arrow for Data Analysis across Disparate Data Sources [requirements] =>
  • A basic undersanding of SQL
  • Familiarity with Python or R
  • Some familiarity with Apache Spark
[overview] => Apache Arrow es un marco de procesamiento de datos en memoria de código abierto. A menudo se usa junto con otras herramientas de ciencia de datos para acceder a almacenes de datos dispares para su análisis. Se integra bien con otras tecnologías, como bases de datos de GPU , bibliotecas y herramientas de aprendizaje automático, motores de ejecución y marcos de visualización de datos. En esta capacitación en vivo dirigida por un instructor en el sitio, los participantes aprenderán cómo integrar Apache Arrow con varios marcos de Data Science para acceder a datos de fuentes de datos dispares. Al final de esta capacitación, los participantes podrán:
  • Instale y configure Apache Arrow en un entorno agrupado distribuido
  • Use Apache Arrow para acceder a datos de fuentes de datos dispares
  • Use Apache Arrow para evitar la necesidad de construir y mantener tuberías complejas de ETL
  • Analice datos a través de fuentes de datos dispares sin tener que consolidarlos en un repositorio centralizado.
Audiencia
  • Científicos de datos
  • Ingenieros de datos
Formato del curso
  • Conferencia parcial, discusión parcial, ejercicios y práctica práctica.
Nota
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => [outline] =>

Introduction

  • Apache Arrow vs Parquet

Installing and Configuring Apache Arrow

Overview of Apache Arrow Features and Architecture

Exploring Data with Pandas and Apache Arrow

Exploring Data with Spark and Apache Arrow

Exploring Data with R and Apache Arrow

Exploring Data with MapD and Apache Arrow

Other Data Analysis Integrations

  • PySpark, Parquet files on S3, and Oracle tables and Elasticsearch indices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037432 [source_title] => Apache Arrow for Data Analysis across Disparate Data Sources [source_language] => en [cert_code] => [weight] => -976 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => arrow ) ) [2] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => apache-arrow [1] => guayaquil ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /apache-arrow/cursos/guayaquil ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/apache-arrow/cursos/guayaquil Undefined array key "nobleprog_price_rounding" /apps/nobleprog-website/includes/functions/course-prices.php:289 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 289 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "nobleprog_price_rounding" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 289 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 5437 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => arrow [venue_id] => ec_15661446 [vfdc] => 175.00 [vadc] => 60.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => arrow ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [arrow] => stdClass Object ( [course_code] => arrow [hr_nid] => 284282 [title] => Apache Arrow for Data Analysis across Disparate Data Sources [requirements] =>
  • A basic undersanding of SQL
  • Familiarity with Python or R
  • Some familiarity with Apache Spark
[overview] => Apache Arrow es un marco de procesamiento de datos en memoria de código abierto. A menudo se usa junto con otras herramientas de ciencia de datos para acceder a almacenes de datos dispares para su análisis. Se integra bien con otras tecnologías, como bases de datos de GPU , bibliotecas y herramientas de aprendizaje automático, motores de ejecución y marcos de visualización de datos. En esta capacitación en vivo dirigida por un instructor en el sitio, los participantes aprenderán cómo integrar Apache Arrow con varios marcos de Data Science para acceder a datos de fuentes de datos dispares. Al final de esta capacitación, los participantes podrán:
  • Instale y configure Apache Arrow en un entorno agrupado distribuido
  • Use Apache Arrow para acceder a datos de fuentes de datos dispares
  • Use Apache Arrow para evitar la necesidad de construir y mantener tuberías complejas de ETL
  • Analice datos a través de fuentes de datos dispares sin tener que consolidarlos en un repositorio centralizado.
Audiencia
  • Científicos de datos
  • Ingenieros de datos
Formato del curso
  • Conferencia parcial, discusión parcial, ejercicios y práctica práctica.
Nota
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => [outline] =>

Introduction

  • Apache Arrow vs Parquet

Installing and Configuring Apache Arrow

Overview of Apache Arrow Features and Architecture

Exploring Data with Pandas and Apache Arrow

Exploring Data with Spark and Apache Arrow

Exploring Data with R and Apache Arrow

Exploring Data with MapD and Apache Arrow

Other Data Analysis Integrations

  • PySpark, Parquet files on S3, and Oracle tables and Elasticsearch indices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037432 [source_title] => Apache Arrow for Data Analysis across Disparate Data Sources [source_language] => en [cert_code] => [weight] => -976 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => arrow ) ) [1] => Array ( [0] => ec_4966 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => arrow [hr_nid] => 284282 [title] => Apache Arrow for Data Analysis across Disparate Data Sources [requirements] =>
  • A basic undersanding of SQL
  • Familiarity with Python or R
  • Some familiarity with Apache Spark
[overview] => Apache Arrow es un marco de procesamiento de datos en memoria de código abierto. A menudo se usa junto con otras herramientas de ciencia de datos para acceder a almacenes de datos dispares para su análisis. Se integra bien con otras tecnologías, como bases de datos de GPU , bibliotecas y herramientas de aprendizaje automático, motores de ejecución y marcos de visualización de datos. En esta capacitación en vivo dirigida por un instructor en el sitio, los participantes aprenderán cómo integrar Apache Arrow con varios marcos de Data Science para acceder a datos de fuentes de datos dispares. Al final de esta capacitación, los participantes podrán:
  • Instale y configure Apache Arrow en un entorno agrupado distribuido
  • Use Apache Arrow para acceder a datos de fuentes de datos dispares
  • Use Apache Arrow para evitar la necesidad de construir y mantener tuberías complejas de ETL
  • Analice datos a través de fuentes de datos dispares sin tener que consolidarlos en un repositorio centralizado.
Audiencia
  • Científicos de datos
  • Ingenieros de datos
Formato del curso
  • Conferencia parcial, discusión parcial, ejercicios y práctica práctica.
Nota
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => [outline] =>

Introduction

  • Apache Arrow vs Parquet

Installing and Configuring Apache Arrow

Overview of Apache Arrow Features and Architecture

Exploring Data with Pandas and Apache Arrow

Exploring Data with Spark and Apache Arrow

Exploring Data with R and Apache Arrow

Exploring Data with MapD and Apache Arrow

Other Data Analysis Integrations

  • PySpark, Parquet files on S3, and Oracle tables and Elasticsearch indices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037432 [source_title] => Apache Arrow for Data Analysis across Disparate Data Sources [source_language] => en [cert_code] => [weight] => -976 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => arrow ) ) [2] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => apache-arrow [1] => guayaquil ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /apache-arrow/cursos/guayaquil ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/apache-arrow/cursos/guayaquil Undefined array key "sdp" /apps/nobleprog-website/includes/functions/course-prices.php:281 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 281 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "sdp" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 281 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 5437 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => arrow [venue_id] => ec_15661447 [vfdc] => 200.00 [vadc] => 50.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => arrow ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [arrow] => stdClass Object ( [course_code] => arrow [hr_nid] => 284282 [title] => Apache Arrow for Data Analysis across Disparate Data Sources [requirements] =>
  • A basic undersanding of SQL
  • Familiarity with Python or R
  • Some familiarity with Apache Spark
[overview] => Apache Arrow es un marco de procesamiento de datos en memoria de código abierto. A menudo se usa junto con otras herramientas de ciencia de datos para acceder a almacenes de datos dispares para su análisis. Se integra bien con otras tecnologías, como bases de datos de GPU , bibliotecas y herramientas de aprendizaje automático, motores de ejecución y marcos de visualización de datos. En esta capacitación en vivo dirigida por un instructor en el sitio, los participantes aprenderán cómo integrar Apache Arrow con varios marcos de Data Science para acceder a datos de fuentes de datos dispares. Al final de esta capacitación, los participantes podrán:
  • Instale y configure Apache Arrow en un entorno agrupado distribuido
  • Use Apache Arrow para acceder a datos de fuentes de datos dispares
  • Use Apache Arrow para evitar la necesidad de construir y mantener tuberías complejas de ETL
  • Analice datos a través de fuentes de datos dispares sin tener que consolidarlos en un repositorio centralizado.
Audiencia
  • Científicos de datos
  • Ingenieros de datos
Formato del curso
  • Conferencia parcial, discusión parcial, ejercicios y práctica práctica.
Nota
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => [outline] =>

Introduction

  • Apache Arrow vs Parquet

Installing and Configuring Apache Arrow

Overview of Apache Arrow Features and Architecture

Exploring Data with Pandas and Apache Arrow

Exploring Data with Spark and Apache Arrow

Exploring Data with R and Apache Arrow

Exploring Data with MapD and Apache Arrow

Other Data Analysis Integrations

  • PySpark, Parquet files on S3, and Oracle tables and Elasticsearch indices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037432 [source_title] => Apache Arrow for Data Analysis across Disparate Data Sources [source_language] => en [cert_code] => [weight] => -976 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => arrow ) ) [1] => Array ( [0] => ec_4966 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => arrow [hr_nid] => 284282 [title] => Apache Arrow for Data Analysis across Disparate Data Sources [requirements] =>
  • A basic undersanding of SQL
  • Familiarity with Python or R
  • Some familiarity with Apache Spark
[overview] => Apache Arrow es un marco de procesamiento de datos en memoria de código abierto. A menudo se usa junto con otras herramientas de ciencia de datos para acceder a almacenes de datos dispares para su análisis. Se integra bien con otras tecnologías, como bases de datos de GPU , bibliotecas y herramientas de aprendizaje automático, motores de ejecución y marcos de visualización de datos. En esta capacitación en vivo dirigida por un instructor en el sitio, los participantes aprenderán cómo integrar Apache Arrow con varios marcos de Data Science para acceder a datos de fuentes de datos dispares. Al final de esta capacitación, los participantes podrán:
  • Instale y configure Apache Arrow en un entorno agrupado distribuido
  • Use Apache Arrow para acceder a datos de fuentes de datos dispares
  • Use Apache Arrow para evitar la necesidad de construir y mantener tuberías complejas de ETL
  • Analice datos a través de fuentes de datos dispares sin tener que consolidarlos en un repositorio centralizado.
Audiencia
  • Científicos de datos
  • Ingenieros de datos
Formato del curso
  • Conferencia parcial, discusión parcial, ejercicios y práctica práctica.
Nota
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => [outline] =>

Introduction

  • Apache Arrow vs Parquet

Installing and Configuring Apache Arrow

Overview of Apache Arrow Features and Architecture

Exploring Data with Pandas and Apache Arrow

Exploring Data with Spark and Apache Arrow

Exploring Data with R and Apache Arrow

Exploring Data with MapD and Apache Arrow

Other Data Analysis Integrations

  • PySpark, Parquet files on S3, and Oracle tables and Elasticsearch indices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037432 [source_title] => Apache Arrow for Data Analysis across Disparate Data Sources [source_language] => en [cert_code] => [weight] => -976 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => arrow ) ) [2] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => apache-arrow [1] => guayaquil ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /apache-arrow/cursos/guayaquil ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/apache-arrow/cursos/guayaquil Undefined array key "nobleprog_default_trainer_journey" /apps/nobleprog-website/includes/functions/course-prices.php:286 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 286 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "nobleprog_default_trainer_journey" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 286 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 5437 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => arrow [venue_id] => ec_15661447 [vfdc] => 200.00 [vadc] => 50.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => arrow ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [arrow] => stdClass Object ( [course_code] => arrow [hr_nid] => 284282 [title] => Apache Arrow for Data Analysis across Disparate Data Sources [requirements] =>
  • A basic undersanding of SQL
  • Familiarity with Python or R
  • Some familiarity with Apache Spark
[overview] => Apache Arrow es un marco de procesamiento de datos en memoria de código abierto. A menudo se usa junto con otras herramientas de ciencia de datos para acceder a almacenes de datos dispares para su análisis. Se integra bien con otras tecnologías, como bases de datos de GPU , bibliotecas y herramientas de aprendizaje automático, motores de ejecución y marcos de visualización de datos. En esta capacitación en vivo dirigida por un instructor en el sitio, los participantes aprenderán cómo integrar Apache Arrow con varios marcos de Data Science para acceder a datos de fuentes de datos dispares. Al final de esta capacitación, los participantes podrán:
  • Instale y configure Apache Arrow en un entorno agrupado distribuido
  • Use Apache Arrow para acceder a datos de fuentes de datos dispares
  • Use Apache Arrow para evitar la necesidad de construir y mantener tuberías complejas de ETL
  • Analice datos a través de fuentes de datos dispares sin tener que consolidarlos en un repositorio centralizado.
Audiencia
  • Científicos de datos
  • Ingenieros de datos
Formato del curso
  • Conferencia parcial, discusión parcial, ejercicios y práctica práctica.
Nota
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => [outline] =>

Introduction

  • Apache Arrow vs Parquet

Installing and Configuring Apache Arrow

Overview of Apache Arrow Features and Architecture

Exploring Data with Pandas and Apache Arrow

Exploring Data with Spark and Apache Arrow

Exploring Data with R and Apache Arrow

Exploring Data with MapD and Apache Arrow

Other Data Analysis Integrations

  • PySpark, Parquet files on S3, and Oracle tables and Elasticsearch indices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037432 [source_title] => Apache Arrow for Data Analysis across Disparate Data Sources [source_language] => en [cert_code] => [weight] => -976 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => arrow ) ) [1] => Array ( [0] => ec_4966 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => arrow [hr_nid] => 284282 [title] => Apache Arrow for Data Analysis across Disparate Data Sources [requirements] =>
  • A basic undersanding of SQL
  • Familiarity with Python or R
  • Some familiarity with Apache Spark
[overview] => Apache Arrow es un marco de procesamiento de datos en memoria de código abierto. A menudo se usa junto con otras herramientas de ciencia de datos para acceder a almacenes de datos dispares para su análisis. Se integra bien con otras tecnologías, como bases de datos de GPU , bibliotecas y herramientas de aprendizaje automático, motores de ejecución y marcos de visualización de datos. En esta capacitación en vivo dirigida por un instructor en el sitio, los participantes aprenderán cómo integrar Apache Arrow con varios marcos de Data Science para acceder a datos de fuentes de datos dispares. Al final de esta capacitación, los participantes podrán:
  • Instale y configure Apache Arrow en un entorno agrupado distribuido
  • Use Apache Arrow para acceder a datos de fuentes de datos dispares
  • Use Apache Arrow para evitar la necesidad de construir y mantener tuberías complejas de ETL
  • Analice datos a través de fuentes de datos dispares sin tener que consolidarlos en un repositorio centralizado.
Audiencia
  • Científicos de datos
  • Ingenieros de datos
Formato del curso
  • Conferencia parcial, discusión parcial, ejercicios y práctica práctica.
Nota
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => [outline] =>

Introduction

  • Apache Arrow vs Parquet

Installing and Configuring Apache Arrow

Overview of Apache Arrow Features and Architecture

Exploring Data with Pandas and Apache Arrow

Exploring Data with Spark and Apache Arrow

Exploring Data with R and Apache Arrow

Exploring Data with MapD and Apache Arrow

Other Data Analysis Integrations

  • PySpark, Parquet files on S3, and Oracle tables and Elasticsearch indices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037432 [source_title] => Apache Arrow for Data Analysis across Disparate Data Sources [source_language] => en [cert_code] => [weight] => -976 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => arrow ) ) [2] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => apache-arrow [1] => guayaquil ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /apache-arrow/cursos/guayaquil ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/apache-arrow/cursos/guayaquil Undefined array key "nobleprog_price_rounding" /apps/nobleprog-website/includes/functions/course-prices.php:289 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 289 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "nobleprog_price_rounding" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 289 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 5437 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => arrow [venue_id] => ec_15661447 [vfdc] => 200.00 [vadc] => 50.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => arrow ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [arrow] => stdClass Object ( [course_code] => arrow [hr_nid] => 284282 [title] => Apache Arrow for Data Analysis across Disparate Data Sources [requirements] =>
  • A basic undersanding of SQL
  • Familiarity with Python or R
  • Some familiarity with Apache Spark
[overview] => Apache Arrow es un marco de procesamiento de datos en memoria de código abierto. A menudo se usa junto con otras herramientas de ciencia de datos para acceder a almacenes de datos dispares para su análisis. Se integra bien con otras tecnologías, como bases de datos de GPU , bibliotecas y herramientas de aprendizaje automático, motores de ejecución y marcos de visualización de datos. En esta capacitación en vivo dirigida por un instructor en el sitio, los participantes aprenderán cómo integrar Apache Arrow con varios marcos de Data Science para acceder a datos de fuentes de datos dispares. Al final de esta capacitación, los participantes podrán:
  • Instale y configure Apache Arrow en un entorno agrupado distribuido
  • Use Apache Arrow para acceder a datos de fuentes de datos dispares
  • Use Apache Arrow para evitar la necesidad de construir y mantener tuberías complejas de ETL
  • Analice datos a través de fuentes de datos dispares sin tener que consolidarlos en un repositorio centralizado.
Audiencia
  • Científicos de datos
  • Ingenieros de datos
Formato del curso
  • Conferencia parcial, discusión parcial, ejercicios y práctica práctica.
Nota
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => [outline] =>

Introduction

  • Apache Arrow vs Parquet

Installing and Configuring Apache Arrow

Overview of Apache Arrow Features and Architecture

Exploring Data with Pandas and Apache Arrow

Exploring Data with Spark and Apache Arrow

Exploring Data with R and Apache Arrow

Exploring Data with MapD and Apache Arrow

Other Data Analysis Integrations

  • PySpark, Parquet files on S3, and Oracle tables and Elasticsearch indices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037432 [source_title] => Apache Arrow for Data Analysis across Disparate Data Sources [source_language] => en [cert_code] => [weight] => -976 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => arrow ) ) [1] => Array ( [0] => ec_4966 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => arrow [hr_nid] => 284282 [title] => Apache Arrow for Data Analysis across Disparate Data Sources [requirements] =>
  • A basic undersanding of SQL
  • Familiarity with Python or R
  • Some familiarity with Apache Spark
[overview] => Apache Arrow es un marco de procesamiento de datos en memoria de código abierto. A menudo se usa junto con otras herramientas de ciencia de datos para acceder a almacenes de datos dispares para su análisis. Se integra bien con otras tecnologías, como bases de datos de GPU , bibliotecas y herramientas de aprendizaje automático, motores de ejecución y marcos de visualización de datos. En esta capacitación en vivo dirigida por un instructor en el sitio, los participantes aprenderán cómo integrar Apache Arrow con varios marcos de Data Science para acceder a datos de fuentes de datos dispares. Al final de esta capacitación, los participantes podrán:
  • Instale y configure Apache Arrow en un entorno agrupado distribuido
  • Use Apache Arrow para acceder a datos de fuentes de datos dispares
  • Use Apache Arrow para evitar la necesidad de construir y mantener tuberías complejas de ETL
  • Analice datos a través de fuentes de datos dispares sin tener que consolidarlos en un repositorio centralizado.
Audiencia
  • Científicos de datos
  • Ingenieros de datos
Formato del curso
  • Conferencia parcial, discusión parcial, ejercicios y práctica práctica.
Nota
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => [outline] =>

Introduction

  • Apache Arrow vs Parquet

Installing and Configuring Apache Arrow

Overview of Apache Arrow Features and Architecture

Exploring Data with Pandas and Apache Arrow

Exploring Data with Spark and Apache Arrow

Exploring Data with R and Apache Arrow

Exploring Data with MapD and Apache Arrow

Other Data Analysis Integrations

  • PySpark, Parquet files on S3, and Oracle tables and Elasticsearch indices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037432 [source_title] => Apache Arrow for Data Analysis across Disparate Data Sources [source_language] => en [cert_code] => [weight] => -976 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => arrow ) ) [2] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => apache-arrow [1] => guayaquil ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /apache-arrow/cursos/guayaquil ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/apache-arrow/cursos/guayaquil Undefined array key "sdp" /apps/nobleprog-website/includes/functions/course-prices.php:281 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 281 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "sdp" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 281 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 5437 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => arrow [venue_id] => ec_15661446 [vfdc] => 175.00 [vadc] => 60.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => arrow ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [arrow] => stdClass Object ( [course_code] => arrow [hr_nid] => 284282 [title] => Apache Arrow for Data Analysis across Disparate Data Sources [requirements] =>
  • A basic undersanding of SQL
  • Familiarity with Python or R
  • Some familiarity with Apache Spark
[overview] => Apache Arrow es un marco de procesamiento de datos en memoria de código abierto. A menudo se usa junto con otras herramientas de ciencia de datos para acceder a almacenes de datos dispares para su análisis. Se integra bien con otras tecnologías, como bases de datos de GPU , bibliotecas y herramientas de aprendizaje automático, motores de ejecución y marcos de visualización de datos. En esta capacitación en vivo dirigida por un instructor en el sitio, los participantes aprenderán cómo integrar Apache Arrow con varios marcos de Data Science para acceder a datos de fuentes de datos dispares. Al final de esta capacitación, los participantes podrán:
  • Instale y configure Apache Arrow en un entorno agrupado distribuido
  • Use Apache Arrow para acceder a datos de fuentes de datos dispares
  • Use Apache Arrow para evitar la necesidad de construir y mantener tuberías complejas de ETL
  • Analice datos a través de fuentes de datos dispares sin tener que consolidarlos en un repositorio centralizado.
Audiencia
  • Científicos de datos
  • Ingenieros de datos
Formato del curso
  • Conferencia parcial, discusión parcial, ejercicios y práctica práctica.
Nota
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => [outline] =>

Introduction

  • Apache Arrow vs Parquet

Installing and Configuring Apache Arrow

Overview of Apache Arrow Features and Architecture

Exploring Data with Pandas and Apache Arrow

Exploring Data with Spark and Apache Arrow

Exploring Data with R and Apache Arrow

Exploring Data with MapD and Apache Arrow

Other Data Analysis Integrations

  • PySpark, Parquet files on S3, and Oracle tables and Elasticsearch indices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037432 [source_title] => Apache Arrow for Data Analysis across Disparate Data Sources [source_language] => en [cert_code] => [weight] => -976 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => arrow ) ) [1] => Array ( [0] => ec_4966 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => arrow [hr_nid] => 284282 [title] => Apache Arrow for Data Analysis across Disparate Data Sources [requirements] =>
  • A basic undersanding of SQL
  • Familiarity with Python or R
  • Some familiarity with Apache Spark
[overview] => Apache Arrow es un marco de procesamiento de datos en memoria de código abierto. A menudo se usa junto con otras herramientas de ciencia de datos para acceder a almacenes de datos dispares para su análisis. Se integra bien con otras tecnologías, como bases de datos de GPU , bibliotecas y herramientas de aprendizaje automático, motores de ejecución y marcos de visualización de datos. En esta capacitación en vivo dirigida por un instructor en el sitio, los participantes aprenderán cómo integrar Apache Arrow con varios marcos de Data Science para acceder a datos de fuentes de datos dispares. Al final de esta capacitación, los participantes podrán:
  • Instale y configure Apache Arrow en un entorno agrupado distribuido
  • Use Apache Arrow para acceder a datos de fuentes de datos dispares
  • Use Apache Arrow para evitar la necesidad de construir y mantener tuberías complejas de ETL
  • Analice datos a través de fuentes de datos dispares sin tener que consolidarlos en un repositorio centralizado.
Audiencia
  • Científicos de datos
  • Ingenieros de datos
Formato del curso
  • Conferencia parcial, discusión parcial, ejercicios y práctica práctica.
Nota
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => [outline] =>

Introduction

  • Apache Arrow vs Parquet

Installing and Configuring Apache Arrow

Overview of Apache Arrow Features and Architecture

Exploring Data with Pandas and Apache Arrow

Exploring Data with Spark and Apache Arrow

Exploring Data with R and Apache Arrow

Exploring Data with MapD and Apache Arrow

Other Data Analysis Integrations

  • PySpark, Parquet files on S3, and Oracle tables and Elasticsearch indices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037432 [source_title] => Apache Arrow for Data Analysis across Disparate Data Sources [source_language] => en [cert_code] => [weight] => -976 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => arrow ) ) [2] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => apache-arrow [1] => guayaquil ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /apache-arrow/cursos/guayaquil ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/apache-arrow/cursos/guayaquil Undefined array key "nobleprog_default_trainer_journey" /apps/nobleprog-website/includes/functions/course-prices.php:286 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 286 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "nobleprog_default_trainer_journey" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 286 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 5437 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => arrow [venue_id] => ec_15661446 [vfdc] => 175.00 [vadc] => 60.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => arrow ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [arrow] => stdClass Object ( [course_code] => arrow [hr_nid] => 284282 [title] => Apache Arrow for Data Analysis across Disparate Data Sources [requirements] =>
  • A basic undersanding of SQL
  • Familiarity with Python or R
  • Some familiarity with Apache Spark
[overview] => Apache Arrow es un marco de procesamiento de datos en memoria de código abierto. A menudo se usa junto con otras herramientas de ciencia de datos para acceder a almacenes de datos dispares para su análisis. Se integra bien con otras tecnologías, como bases de datos de GPU , bibliotecas y herramientas de aprendizaje automático, motores de ejecución y marcos de visualización de datos. En esta capacitación en vivo dirigida por un instructor en el sitio, los participantes aprenderán cómo integrar Apache Arrow con varios marcos de Data Science para acceder a datos de fuentes de datos dispares. Al final de esta capacitación, los participantes podrán:
  • Instale y configure Apache Arrow en un entorno agrupado distribuido
  • Use Apache Arrow para acceder a datos de fuentes de datos dispares
  • Use Apache Arrow para evitar la necesidad de construir y mantener tuberías complejas de ETL
  • Analice datos a través de fuentes de datos dispares sin tener que consolidarlos en un repositorio centralizado.
Audiencia
  • Científicos de datos
  • Ingenieros de datos
Formato del curso
  • Conferencia parcial, discusión parcial, ejercicios y práctica práctica.
Nota
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => [outline] =>

Introduction

  • Apache Arrow vs Parquet

Installing and Configuring Apache Arrow

Overview of Apache Arrow Features and Architecture

Exploring Data with Pandas and Apache Arrow

Exploring Data with Spark and Apache Arrow

Exploring Data with R and Apache Arrow

Exploring Data with MapD and Apache Arrow

Other Data Analysis Integrations

  • PySpark, Parquet files on S3, and Oracle tables and Elasticsearch indices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037432 [source_title] => Apache Arrow for Data Analysis across Disparate Data Sources [source_language] => en [cert_code] => [weight] => -976 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => arrow ) ) [1] => Array ( [0] => ec_4966 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => arrow [hr_nid] => 284282 [title] => Apache Arrow for Data Analysis across Disparate Data Sources [requirements] =>
  • A basic undersanding of SQL
  • Familiarity with Python or R
  • Some familiarity with Apache Spark
[overview] => Apache Arrow es un marco de procesamiento de datos en memoria de código abierto. A menudo se usa junto con otras herramientas de ciencia de datos para acceder a almacenes de datos dispares para su análisis. Se integra bien con otras tecnologías, como bases de datos de GPU , bibliotecas y herramientas de aprendizaje automático, motores de ejecución y marcos de visualización de datos. En esta capacitación en vivo dirigida por un instructor en el sitio, los participantes aprenderán cómo integrar Apache Arrow con varios marcos de Data Science para acceder a datos de fuentes de datos dispares. Al final de esta capacitación, los participantes podrán:
  • Instale y configure Apache Arrow en un entorno agrupado distribuido
  • Use Apache Arrow para acceder a datos de fuentes de datos dispares
  • Use Apache Arrow para evitar la necesidad de construir y mantener tuberías complejas de ETL
  • Analice datos a través de fuentes de datos dispares sin tener que consolidarlos en un repositorio centralizado.
Audiencia
  • Científicos de datos
  • Ingenieros de datos
Formato del curso
  • Conferencia parcial, discusión parcial, ejercicios y práctica práctica.
Nota
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => [outline] =>

Introduction

  • Apache Arrow vs Parquet

Installing and Configuring Apache Arrow

Overview of Apache Arrow Features and Architecture

Exploring Data with Pandas and Apache Arrow

Exploring Data with Spark and Apache Arrow

Exploring Data with R and Apache Arrow

Exploring Data with MapD and Apache Arrow

Other Data Analysis Integrations

  • PySpark, Parquet files on S3, and Oracle tables and Elasticsearch indices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037432 [source_title] => Apache Arrow for Data Analysis across Disparate Data Sources [source_language] => en [cert_code] => [weight] => -976 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => arrow ) ) [2] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => apache-arrow [1] => guayaquil ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /apache-arrow/cursos/guayaquil ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/apache-arrow/cursos/guayaquil Undefined array key "nobleprog_price_rounding" /apps/nobleprog-website/includes/functions/course-prices.php:289 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 289 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "nobleprog_price_rounding" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 289 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 5437 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => arrow [venue_id] => ec_15661446 [vfdc] => 175.00 [vadc] => 60.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => arrow ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [arrow] => stdClass Object ( [course_code] => arrow [hr_nid] => 284282 [title] => Apache Arrow for Data Analysis across Disparate Data Sources [requirements] =>
  • A basic undersanding of SQL
  • Familiarity with Python or R
  • Some familiarity with Apache Spark
[overview] => Apache Arrow es un marco de procesamiento de datos en memoria de código abierto. A menudo se usa junto con otras herramientas de ciencia de datos para acceder a almacenes de datos dispares para su análisis. Se integra bien con otras tecnologías, como bases de datos de GPU , bibliotecas y herramientas de aprendizaje automático, motores de ejecución y marcos de visualización de datos. En esta capacitación en vivo dirigida por un instructor en el sitio, los participantes aprenderán cómo integrar Apache Arrow con varios marcos de Data Science para acceder a datos de fuentes de datos dispares. Al final de esta capacitación, los participantes podrán:
  • Instale y configure Apache Arrow en un entorno agrupado distribuido
  • Use Apache Arrow para acceder a datos de fuentes de datos dispares
  • Use Apache Arrow para evitar la necesidad de construir y mantener tuberías complejas de ETL
  • Analice datos a través de fuentes de datos dispares sin tener que consolidarlos en un repositorio centralizado.
Audiencia
  • Científicos de datos
  • Ingenieros de datos
Formato del curso
  • Conferencia parcial, discusión parcial, ejercicios y práctica práctica.
Nota
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => [outline] =>

Introduction

  • Apache Arrow vs Parquet

Installing and Configuring Apache Arrow

Overview of Apache Arrow Features and Architecture

Exploring Data with Pandas and Apache Arrow

Exploring Data with Spark and Apache Arrow

Exploring Data with R and Apache Arrow

Exploring Data with MapD and Apache Arrow

Other Data Analysis Integrations

  • PySpark, Parquet files on S3, and Oracle tables and Elasticsearch indices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037432 [source_title] => Apache Arrow for Data Analysis across Disparate Data Sources [source_language] => en [cert_code] => [weight] => -976 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => arrow ) ) [1] => Array ( [0] => ec_4966 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => arrow [hr_nid] => 284282 [title] => Apache Arrow for Data Analysis across Disparate Data Sources [requirements] =>
  • A basic undersanding of SQL
  • Familiarity with Python or R
  • Some familiarity with Apache Spark
[overview] => Apache Arrow es un marco de procesamiento de datos en memoria de código abierto. A menudo se usa junto con otras herramientas de ciencia de datos para acceder a almacenes de datos dispares para su análisis. Se integra bien con otras tecnologías, como bases de datos de GPU , bibliotecas y herramientas de aprendizaje automático, motores de ejecución y marcos de visualización de datos. En esta capacitación en vivo dirigida por un instructor en el sitio, los participantes aprenderán cómo integrar Apache Arrow con varios marcos de Data Science para acceder a datos de fuentes de datos dispares. Al final de esta capacitación, los participantes podrán:
  • Instale y configure Apache Arrow en un entorno agrupado distribuido
  • Use Apache Arrow para acceder a datos de fuentes de datos dispares
  • Use Apache Arrow para evitar la necesidad de construir y mantener tuberías complejas de ETL
  • Analice datos a través de fuentes de datos dispares sin tener que consolidarlos en un repositorio centralizado.
Audiencia
  • Científicos de datos
  • Ingenieros de datos
Formato del curso
  • Conferencia parcial, discusión parcial, ejercicios y práctica práctica.
Nota
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => [outline] =>

Introduction

  • Apache Arrow vs Parquet

Installing and Configuring Apache Arrow

Overview of Apache Arrow Features and Architecture

Exploring Data with Pandas and Apache Arrow

Exploring Data with Spark and Apache Arrow

Exploring Data with R and Apache Arrow

Exploring Data with MapD and Apache Arrow

Other Data Analysis Integrations

  • PySpark, Parquet files on S3, and Oracle tables and Elasticsearch indices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037432 [source_title] => Apache Arrow for Data Analysis across Disparate Data Sources [source_language] => en [cert_code] => [weight] => -976 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => arrow ) ) [2] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => apache-arrow [1] => guayaquil ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /apache-arrow/cursos/guayaquil ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/apache-arrow/cursos/guayaquil Undefined array key "sdp" /apps/nobleprog-website/includes/functions/course-prices.php:281 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 281 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "sdp" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 281 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 5437 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => arrow [venue_id] => ec_15661447 [vfdc] => 200.00 [vadc] => 50.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => arrow ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [arrow] => stdClass Object ( [course_code] => arrow [hr_nid] => 284282 [title] => Apache Arrow for Data Analysis across Disparate Data Sources [requirements] =>
  • A basic undersanding of SQL
  • Familiarity with Python or R
  • Some familiarity with Apache Spark
[overview] => Apache Arrow es un marco de procesamiento de datos en memoria de código abierto. A menudo se usa junto con otras herramientas de ciencia de datos para acceder a almacenes de datos dispares para su análisis. Se integra bien con otras tecnologías, como bases de datos de GPU , bibliotecas y herramientas de aprendizaje automático, motores de ejecución y marcos de visualización de datos. En esta capacitación en vivo dirigida por un instructor en el sitio, los participantes aprenderán cómo integrar Apache Arrow con varios marcos de Data Science para acceder a datos de fuentes de datos dispares. Al final de esta capacitación, los participantes podrán:
  • Instale y configure Apache Arrow en un entorno agrupado distribuido
  • Use Apache Arrow para acceder a datos de fuentes de datos dispares
  • Use Apache Arrow para evitar la necesidad de construir y mantener tuberías complejas de ETL
  • Analice datos a través de fuentes de datos dispares sin tener que consolidarlos en un repositorio centralizado.
Audiencia
  • Científicos de datos
  • Ingenieros de datos
Formato del curso
  • Conferencia parcial, discusión parcial, ejercicios y práctica práctica.
Nota
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => [outline] =>

Introduction

  • Apache Arrow vs Parquet

Installing and Configuring Apache Arrow

Overview of Apache Arrow Features and Architecture

Exploring Data with Pandas and Apache Arrow

Exploring Data with Spark and Apache Arrow

Exploring Data with R and Apache Arrow

Exploring Data with MapD and Apache Arrow

Other Data Analysis Integrations

  • PySpark, Parquet files on S3, and Oracle tables and Elasticsearch indices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037432 [source_title] => Apache Arrow for Data Analysis across Disparate Data Sources [source_language] => en [cert_code] => [weight] => -976 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => arrow ) ) [1] => Array ( [0] => ec_4966 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => arrow [hr_nid] => 284282 [title] => Apache Arrow for Data Analysis across Disparate Data Sources [requirements] =>
  • A basic undersanding of SQL
  • Familiarity with Python or R
  • Some familiarity with Apache Spark
[overview] => Apache Arrow es un marco de procesamiento de datos en memoria de código abierto. A menudo se usa junto con otras herramientas de ciencia de datos para acceder a almacenes de datos dispares para su análisis. Se integra bien con otras tecnologías, como bases de datos de GPU , bibliotecas y herramientas de aprendizaje automático, motores de ejecución y marcos de visualización de datos. En esta capacitación en vivo dirigida por un instructor en el sitio, los participantes aprenderán cómo integrar Apache Arrow con varios marcos de Data Science para acceder a datos de fuentes de datos dispares. Al final de esta capacitación, los participantes podrán:
  • Instale y configure Apache Arrow en un entorno agrupado distribuido
  • Use Apache Arrow para acceder a datos de fuentes de datos dispares
  • Use Apache Arrow para evitar la necesidad de construir y mantener tuberías complejas de ETL
  • Analice datos a través de fuentes de datos dispares sin tener que consolidarlos en un repositorio centralizado.
Audiencia
  • Científicos de datos
  • Ingenieros de datos
Formato del curso
  • Conferencia parcial, discusión parcial, ejercicios y práctica práctica.
Nota
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => [outline] =>

Introduction

  • Apache Arrow vs Parquet

Installing and Configuring Apache Arrow

Overview of Apache Arrow Features and Architecture

Exploring Data with Pandas and Apache Arrow

Exploring Data with Spark and Apache Arrow

Exploring Data with R and Apache Arrow

Exploring Data with MapD and Apache Arrow

Other Data Analysis Integrations

  • PySpark, Parquet files on S3, and Oracle tables and Elasticsearch indices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037432 [source_title] => Apache Arrow for Data Analysis across Disparate Data Sources [source_language] => en [cert_code] => [weight] => -976 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => arrow ) ) [2] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => apache-arrow [1] => guayaquil ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /apache-arrow/cursos/guayaquil ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/apache-arrow/cursos/guayaquil Undefined array key "nobleprog_default_trainer_journey" /apps/nobleprog-website/includes/functions/course-prices.php:286 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 286 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "nobleprog_default_trainer_journey" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 286 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 5437 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => arrow [venue_id] => ec_15661447 [vfdc] => 200.00 [vadc] => 50.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => arrow ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [arrow] => stdClass Object ( [course_code] => arrow [hr_nid] => 284282 [title] => Apache Arrow for Data Analysis across Disparate Data Sources [requirements] =>
  • A basic undersanding of SQL
  • Familiarity with Python or R
  • Some familiarity with Apache Spark
[overview] => Apache Arrow es un marco de procesamiento de datos en memoria de código abierto. A menudo se usa junto con otras herramientas de ciencia de datos para acceder a almacenes de datos dispares para su análisis. Se integra bien con otras tecnologías, como bases de datos de GPU , bibliotecas y herramientas de aprendizaje automático, motores de ejecución y marcos de visualización de datos. En esta capacitación en vivo dirigida por un instructor en el sitio, los participantes aprenderán cómo integrar Apache Arrow con varios marcos de Data Science para acceder a datos de fuentes de datos dispares. Al final de esta capacitación, los participantes podrán:
  • Instale y configure Apache Arrow en un entorno agrupado distribuido
  • Use Apache Arrow para acceder a datos de fuentes de datos dispares
  • Use Apache Arrow para evitar la necesidad de construir y mantener tuberías complejas de ETL
  • Analice datos a través de fuentes de datos dispares sin tener que consolidarlos en un repositorio centralizado.
Audiencia
  • Científicos de datos
  • Ingenieros de datos
Formato del curso
  • Conferencia parcial, discusión parcial, ejercicios y práctica práctica.
Nota
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => [outline] =>

Introduction

  • Apache Arrow vs Parquet

Installing and Configuring Apache Arrow

Overview of Apache Arrow Features and Architecture

Exploring Data with Pandas and Apache Arrow

Exploring Data with Spark and Apache Arrow

Exploring Data with R and Apache Arrow

Exploring Data with MapD and Apache Arrow

Other Data Analysis Integrations

  • PySpark, Parquet files on S3, and Oracle tables and Elasticsearch indices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037432 [source_title] => Apache Arrow for Data Analysis across Disparate Data Sources [source_language] => en [cert_code] => [weight] => -976 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => arrow ) ) [1] => Array ( [0] => ec_4966 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => arrow [hr_nid] => 284282 [title] => Apache Arrow for Data Analysis across Disparate Data Sources [requirements] =>
  • A basic undersanding of SQL
  • Familiarity with Python or R
  • Some familiarity with Apache Spark
[overview] => Apache Arrow es un marco de procesamiento de datos en memoria de código abierto. A menudo se usa junto con otras herramientas de ciencia de datos para acceder a almacenes de datos dispares para su análisis. Se integra bien con otras tecnologías, como bases de datos de GPU , bibliotecas y herramientas de aprendizaje automático, motores de ejecución y marcos de visualización de datos. En esta capacitación en vivo dirigida por un instructor en el sitio, los participantes aprenderán cómo integrar Apache Arrow con varios marcos de Data Science para acceder a datos de fuentes de datos dispares. Al final de esta capacitación, los participantes podrán:
  • Instale y configure Apache Arrow en un entorno agrupado distribuido
  • Use Apache Arrow para acceder a datos de fuentes de datos dispares
  • Use Apache Arrow para evitar la necesidad de construir y mantener tuberías complejas de ETL
  • Analice datos a través de fuentes de datos dispares sin tener que consolidarlos en un repositorio centralizado.
Audiencia
  • Científicos de datos
  • Ingenieros de datos
Formato del curso
  • Conferencia parcial, discusión parcial, ejercicios y práctica práctica.
Nota
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => [outline] =>

Introduction

  • Apache Arrow vs Parquet

Installing and Configuring Apache Arrow

Overview of Apache Arrow Features and Architecture

Exploring Data with Pandas and Apache Arrow

Exploring Data with Spark and Apache Arrow

Exploring Data with R and Apache Arrow

Exploring Data with MapD and Apache Arrow

Other Data Analysis Integrations

  • PySpark, Parquet files on S3, and Oracle tables and Elasticsearch indices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037432 [source_title] => Apache Arrow for Data Analysis across Disparate Data Sources [source_language] => en [cert_code] => [weight] => -976 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => arrow ) ) [2] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => apache-arrow [1] => guayaquil ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /apache-arrow/cursos/guayaquil ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/apache-arrow/cursos/guayaquil Undefined array key "nobleprog_price_rounding" /apps/nobleprog-website/includes/functions/course-prices.php:289 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 289 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "nobleprog_price_rounding" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 289 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 5437 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => arrow [venue_id] => ec_15661447 [vfdc] => 200.00 [vadc] => 50.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => arrow ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [arrow] => stdClass Object ( [course_code] => arrow [hr_nid] => 284282 [title] => Apache Arrow for Data Analysis across Disparate Data Sources [requirements] =>
  • A basic undersanding of SQL
  • Familiarity with Python or R
  • Some familiarity with Apache Spark
[overview] => Apache Arrow es un marco de procesamiento de datos en memoria de código abierto. A menudo se usa junto con otras herramientas de ciencia de datos para acceder a almacenes de datos dispares para su análisis. Se integra bien con otras tecnologías, como bases de datos de GPU , bibliotecas y herramientas de aprendizaje automático, motores de ejecución y marcos de visualización de datos. En esta capacitación en vivo dirigida por un instructor en el sitio, los participantes aprenderán cómo integrar Apache Arrow con varios marcos de Data Science para acceder a datos de fuentes de datos dispares. Al final de esta capacitación, los participantes podrán:
  • Instale y configure Apache Arrow en un entorno agrupado distribuido
  • Use Apache Arrow para acceder a datos de fuentes de datos dispares
  • Use Apache Arrow para evitar la necesidad de construir y mantener tuberías complejas de ETL
  • Analice datos a través de fuentes de datos dispares sin tener que consolidarlos en un repositorio centralizado.
Audiencia
  • Científicos de datos
  • Ingenieros de datos
Formato del curso
  • Conferencia parcial, discusión parcial, ejercicios y práctica práctica.
Nota
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => [outline] =>

Introduction

  • Apache Arrow vs Parquet

Installing and Configuring Apache Arrow

Overview of Apache Arrow Features and Architecture

Exploring Data with Pandas and Apache Arrow

Exploring Data with Spark and Apache Arrow

Exploring Data with R and Apache Arrow

Exploring Data with MapD and Apache Arrow

Other Data Analysis Integrations

  • PySpark, Parquet files on S3, and Oracle tables and Elasticsearch indices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037432 [source_title] => Apache Arrow for Data Analysis across Disparate Data Sources [source_language] => en [cert_code] => [weight] => -976 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => arrow ) ) [1] => Array ( [0] => ec_4966 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => arrow [hr_nid] => 284282 [title] => Apache Arrow for Data Analysis across Disparate Data Sources [requirements] =>
  • A basic undersanding of SQL
  • Familiarity with Python or R
  • Some familiarity with Apache Spark
[overview] => Apache Arrow es un marco de procesamiento de datos en memoria de código abierto. A menudo se usa junto con otras herramientas de ciencia de datos para acceder a almacenes de datos dispares para su análisis. Se integra bien con otras tecnologías, como bases de datos de GPU , bibliotecas y herramientas de aprendizaje automático, motores de ejecución y marcos de visualización de datos. En esta capacitación en vivo dirigida por un instructor en el sitio, los participantes aprenderán cómo integrar Apache Arrow con varios marcos de Data Science para acceder a datos de fuentes de datos dispares. Al final de esta capacitación, los participantes podrán:
  • Instale y configure Apache Arrow en un entorno agrupado distribuido
  • Use Apache Arrow para acceder a datos de fuentes de datos dispares
  • Use Apache Arrow para evitar la necesidad de construir y mantener tuberías complejas de ETL
  • Analice datos a través de fuentes de datos dispares sin tener que consolidarlos en un repositorio centralizado.
Audiencia
  • Científicos de datos
  • Ingenieros de datos
Formato del curso
  • Conferencia parcial, discusión parcial, ejercicios y práctica práctica.
Nota
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => [outline] =>

Introduction

  • Apache Arrow vs Parquet

Installing and Configuring Apache Arrow

Overview of Apache Arrow Features and Architecture

Exploring Data with Pandas and Apache Arrow

Exploring Data with Spark and Apache Arrow

Exploring Data with R and Apache Arrow

Exploring Data with MapD and Apache Arrow

Other Data Analysis Integrations

  • PySpark, Parquet files on S3, and Oracle tables and Elasticsearch indices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037432 [source_title] => Apache Arrow for Data Analysis across Disparate Data Sources [source_language] => en [cert_code] => [weight] => -976 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => arrow ) ) [2] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => apache-arrow [1] => guayaquil ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /apache-arrow/cursos/guayaquil ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/apache-arrow/cursos/guayaquil Undefined array key "sdp" /apps/nobleprog-website/includes/functions/course-prices.php:281 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 281 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "sdp" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 281 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 5437 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => arrow [venue_id] => ec_15661446 [vfdc] => 175.00 [vadc] => 60.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => arrow ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [arrow] => stdClass Object ( [course_code] => arrow [hr_nid] => 284282 [title] => Apache Arrow for Data Analysis across Disparate Data Sources [requirements] =>
  • A basic undersanding of SQL
  • Familiarity with Python or R
  • Some familiarity with Apache Spark
[overview] => Apache Arrow es un marco de procesamiento de datos en memoria de código abierto. A menudo se usa junto con otras herramientas de ciencia de datos para acceder a almacenes de datos dispares para su análisis. Se integra bien con otras tecnologías, como bases de datos de GPU , bibliotecas y herramientas de aprendizaje automático, motores de ejecución y marcos de visualización de datos. En esta capacitación en vivo dirigida por un instructor en el sitio, los participantes aprenderán cómo integrar Apache Arrow con varios marcos de Data Science para acceder a datos de fuentes de datos dispares. Al final de esta capacitación, los participantes podrán:
  • Instale y configure Apache Arrow en un entorno agrupado distribuido
  • Use Apache Arrow para acceder a datos de fuentes de datos dispares
  • Use Apache Arrow para evitar la necesidad de construir y mantener tuberías complejas de ETL
  • Analice datos a través de fuentes de datos dispares sin tener que consolidarlos en un repositorio centralizado.
Audiencia
  • Científicos de datos
  • Ingenieros de datos
Formato del curso
  • Conferencia parcial, discusión parcial, ejercicios y práctica práctica.
Nota
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => [outline] =>

Introduction

  • Apache Arrow vs Parquet

Installing and Configuring Apache Arrow

Overview of Apache Arrow Features and Architecture

Exploring Data with Pandas and Apache Arrow

Exploring Data with Spark and Apache Arrow

Exploring Data with R and Apache Arrow

Exploring Data with MapD and Apache Arrow

Other Data Analysis Integrations

  • PySpark, Parquet files on S3, and Oracle tables and Elasticsearch indices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037432 [source_title] => Apache Arrow for Data Analysis across Disparate Data Sources [source_language] => en [cert_code] => [weight] => -976 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => arrow ) ) [1] => Array ( [0] => ec_4966 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => arrow [hr_nid] => 284282 [title] => Apache Arrow for Data Analysis across Disparate Data Sources [requirements] =>
  • A basic undersanding of SQL
  • Familiarity with Python or R
  • Some familiarity with Apache Spark
[overview] => Apache Arrow es un marco de procesamiento de datos en memoria de código abierto. A menudo se usa junto con otras herramientas de ciencia de datos para acceder a almacenes de datos dispares para su análisis. Se integra bien con otras tecnologías, como bases de datos de GPU , bibliotecas y herramientas de aprendizaje automático, motores de ejecución y marcos de visualización de datos. En esta capacitación en vivo dirigida por un instructor en el sitio, los participantes aprenderán cómo integrar Apache Arrow con varios marcos de Data Science para acceder a datos de fuentes de datos dispares. Al final de esta capacitación, los participantes podrán:
  • Instale y configure Apache Arrow en un entorno agrupado distribuido
  • Use Apache Arrow para acceder a datos de fuentes de datos dispares
  • Use Apache Arrow para evitar la necesidad de construir y mantener tuberías complejas de ETL
  • Analice datos a través de fuentes de datos dispares sin tener que consolidarlos en un repositorio centralizado.
Audiencia
  • Científicos de datos
  • Ingenieros de datos
Formato del curso
  • Conferencia parcial, discusión parcial, ejercicios y práctica práctica.
Nota
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => [outline] =>

Introduction

  • Apache Arrow vs Parquet

Installing and Configuring Apache Arrow

Overview of Apache Arrow Features and Architecture

Exploring Data with Pandas and Apache Arrow

Exploring Data with Spark and Apache Arrow

Exploring Data with R and Apache Arrow

Exploring Data with MapD and Apache Arrow

Other Data Analysis Integrations

  • PySpark, Parquet files on S3, and Oracle tables and Elasticsearch indices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037432 [source_title] => Apache Arrow for Data Analysis across Disparate Data Sources [source_language] => en [cert_code] => [weight] => -976 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => arrow ) ) [2] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => apache-arrow [1] => guayaquil ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /apache-arrow/cursos/guayaquil ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/apache-arrow/cursos/guayaquil Undefined array key "nobleprog_default_trainer_journey" /apps/nobleprog-website/includes/functions/course-prices.php:286 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 286 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "nobleprog_default_trainer_journey" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 286 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 5437 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => arrow [venue_id] => ec_15661446 [vfdc] => 175.00 [vadc] => 60.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => arrow ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [arrow] => stdClass Object ( [course_code] => arrow [hr_nid] => 284282 [title] => Apache Arrow for Data Analysis across Disparate Data Sources [requirements] =>
  • A basic undersanding of SQL
  • Familiarity with Python or R
  • Some familiarity with Apache Spark
[overview] => Apache Arrow es un marco de procesamiento de datos en memoria de código abierto. A menudo se usa junto con otras herramientas de ciencia de datos para acceder a almacenes de datos dispares para su análisis. Se integra bien con otras tecnologías, como bases de datos de GPU , bibliotecas y herramientas de aprendizaje automático, motores de ejecución y marcos de visualización de datos. En esta capacitación en vivo dirigida por un instructor en el sitio, los participantes aprenderán cómo integrar Apache Arrow con varios marcos de Data Science para acceder a datos de fuentes de datos dispares. Al final de esta capacitación, los participantes podrán:
  • Instale y configure Apache Arrow en un entorno agrupado distribuido
  • Use Apache Arrow para acceder a datos de fuentes de datos dispares
  • Use Apache Arrow para evitar la necesidad de construir y mantener tuberías complejas de ETL
  • Analice datos a través de fuentes de datos dispares sin tener que consolidarlos en un repositorio centralizado.
Audiencia
  • Científicos de datos
  • Ingenieros de datos
Formato del curso
  • Conferencia parcial, discusión parcial, ejercicios y práctica práctica.
Nota
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => [outline] =>

Introduction

  • Apache Arrow vs Parquet

Installing and Configuring Apache Arrow

Overview of Apache Arrow Features and Architecture

Exploring Data with Pandas and Apache Arrow

Exploring Data with Spark and Apache Arrow

Exploring Data with R and Apache Arrow

Exploring Data with MapD and Apache Arrow

Other Data Analysis Integrations

  • PySpark, Parquet files on S3, and Oracle tables and Elasticsearch indices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037432 [source_title] => Apache Arrow for Data Analysis across Disparate Data Sources [source_language] => en [cert_code] => [weight] => -976 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => arrow ) ) [1] => Array ( [0] => ec_4966 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => arrow [hr_nid] => 284282 [title] => Apache Arrow for Data Analysis across Disparate Data Sources [requirements] =>
  • A basic undersanding of SQL
  • Familiarity with Python or R
  • Some familiarity with Apache Spark
[overview] => Apache Arrow es un marco de procesamiento de datos en memoria de código abierto. A menudo se usa junto con otras herramientas de ciencia de datos para acceder a almacenes de datos dispares para su análisis. Se integra bien con otras tecnologías, como bases de datos de GPU , bibliotecas y herramientas de aprendizaje automático, motores de ejecución y marcos de visualización de datos. En esta capacitación en vivo dirigida por un instructor en el sitio, los participantes aprenderán cómo integrar Apache Arrow con varios marcos de Data Science para acceder a datos de fuentes de datos dispares. Al final de esta capacitación, los participantes podrán:
  • Instale y configure Apache Arrow en un entorno agrupado distribuido
  • Use Apache Arrow para acceder a datos de fuentes de datos dispares
  • Use Apache Arrow para evitar la necesidad de construir y mantener tuberías complejas de ETL
  • Analice datos a través de fuentes de datos dispares sin tener que consolidarlos en un repositorio centralizado.
Audiencia
  • Científicos de datos
  • Ingenieros de datos
Formato del curso
  • Conferencia parcial, discusión parcial, ejercicios y práctica práctica.
Nota
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => [outline] =>

Introduction

  • Apache Arrow vs Parquet

Installing and Configuring Apache Arrow

Overview of Apache Arrow Features and Architecture

Exploring Data with Pandas and Apache Arrow

Exploring Data with Spark and Apache Arrow

Exploring Data with R and Apache Arrow

Exploring Data with MapD and Apache Arrow

Other Data Analysis Integrations

  • PySpark, Parquet files on S3, and Oracle tables and Elasticsearch indices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037432 [source_title] => Apache Arrow for Data Analysis across Disparate Data Sources [source_language] => en [cert_code] => [weight] => -976 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => arrow ) ) [2] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => apache-arrow [1] => guayaquil ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /apache-arrow/cursos/guayaquil ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/apache-arrow/cursos/guayaquil Undefined array key "nobleprog_price_rounding" /apps/nobleprog-website/includes/functions/course-prices.php:289 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 289 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "nobleprog_price_rounding" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 289 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 5437 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => arrow [venue_id] => ec_15661446 [vfdc] => 175.00 [vadc] => 60.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => arrow ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [arrow] => stdClass Object ( [course_code] => arrow [hr_nid] => 284282 [title] => Apache Arrow for Data Analysis across Disparate Data Sources [requirements] =>
  • A basic undersanding of SQL
  • Familiarity with Python or R
  • Some familiarity with Apache Spark
[overview] => Apache Arrow es un marco de procesamiento de datos en memoria de código abierto. A menudo se usa junto con otras herramientas de ciencia de datos para acceder a almacenes de datos dispares para su análisis. Se integra bien con otras tecnologías, como bases de datos de GPU , bibliotecas y herramientas de aprendizaje automático, motores de ejecución y marcos de visualización de datos. En esta capacitación en vivo dirigida por un instructor en el sitio, los participantes aprenderán cómo integrar Apache Arrow con varios marcos de Data Science para acceder a datos de fuentes de datos dispares. Al final de esta capacitación, los participantes podrán:
  • Instale y configure Apache Arrow en un entorno agrupado distribuido
  • Use Apache Arrow para acceder a datos de fuentes de datos dispares
  • Use Apache Arrow para evitar la necesidad de construir y mantener tuberías complejas de ETL
  • Analice datos a través de fuentes de datos dispares sin tener que consolidarlos en un repositorio centralizado.
Audiencia
  • Científicos de datos
  • Ingenieros de datos
Formato del curso
  • Conferencia parcial, discusión parcial, ejercicios y práctica práctica.
Nota
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => [outline] =>

Introduction

  • Apache Arrow vs Parquet

Installing and Configuring Apache Arrow

Overview of Apache Arrow Features and Architecture

Exploring Data with Pandas and Apache Arrow

Exploring Data with Spark and Apache Arrow

Exploring Data with R and Apache Arrow

Exploring Data with MapD and Apache Arrow

Other Data Analysis Integrations

  • PySpark, Parquet files on S3, and Oracle tables and Elasticsearch indices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037432 [source_title] => Apache Arrow for Data Analysis across Disparate Data Sources [source_language] => en [cert_code] => [weight] => -976 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => arrow ) ) [1] => Array ( [0] => ec_4966 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => arrow [hr_nid] => 284282 [title] => Apache Arrow for Data Analysis across Disparate Data Sources [requirements] =>
  • A basic undersanding of SQL
  • Familiarity with Python or R
  • Some familiarity with Apache Spark
[overview] => Apache Arrow es un marco de procesamiento de datos en memoria de código abierto. A menudo se usa junto con otras herramientas de ciencia de datos para acceder a almacenes de datos dispares para su análisis. Se integra bien con otras tecnologías, como bases de datos de GPU , bibliotecas y herramientas de aprendizaje automático, motores de ejecución y marcos de visualización de datos. En esta capacitación en vivo dirigida por un instructor en el sitio, los participantes aprenderán cómo integrar Apache Arrow con varios marcos de Data Science para acceder a datos de fuentes de datos dispares. Al final de esta capacitación, los participantes podrán:
  • Instale y configure Apache Arrow en un entorno agrupado distribuido
  • Use Apache Arrow para acceder a datos de fuentes de datos dispares
  • Use Apache Arrow para evitar la necesidad de construir y mantener tuberías complejas de ETL
  • Analice datos a través de fuentes de datos dispares sin tener que consolidarlos en un repositorio centralizado.
Audiencia
  • Científicos de datos
  • Ingenieros de datos
Formato del curso
  • Conferencia parcial, discusión parcial, ejercicios y práctica práctica.
Nota
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => [outline] =>

Introduction

  • Apache Arrow vs Parquet

Installing and Configuring Apache Arrow

Overview of Apache Arrow Features and Architecture

Exploring Data with Pandas and Apache Arrow

Exploring Data with Spark and Apache Arrow

Exploring Data with R and Apache Arrow

Exploring Data with MapD and Apache Arrow

Other Data Analysis Integrations

  • PySpark, Parquet files on S3, and Oracle tables and Elasticsearch indices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037432 [source_title] => Apache Arrow for Data Analysis across Disparate Data Sources [source_language] => en [cert_code] => [weight] => -976 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => arrow ) ) [2] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => apache-arrow [1] => guayaquil ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /apache-arrow/cursos/guayaquil ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/apache-arrow/cursos/guayaquil Undefined array key "sdp" /apps/nobleprog-website/includes/functions/course-prices.php:281 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 281 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "sdp" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 281 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 5437 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => arrow [venue_id] => ec_15661447 [vfdc] => 200.00 [vadc] => 50.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => arrow ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [arrow] => stdClass Object ( [course_code] => arrow [hr_nid] => 284282 [title] => Apache Arrow for Data Analysis across Disparate Data Sources [requirements] =>
  • A basic undersanding of SQL
  • Familiarity with Python or R
  • Some familiarity with Apache Spark
[overview] => Apache Arrow es un marco de procesamiento de datos en memoria de código abierto. A menudo se usa junto con otras herramientas de ciencia de datos para acceder a almacenes de datos dispares para su análisis. Se integra bien con otras tecnologías, como bases de datos de GPU , bibliotecas y herramientas de aprendizaje automático, motores de ejecución y marcos de visualización de datos. En esta capacitación en vivo dirigida por un instructor en el sitio, los participantes aprenderán cómo integrar Apache Arrow con varios marcos de Data Science para acceder a datos de fuentes de datos dispares. Al final de esta capacitación, los participantes podrán:
  • Instale y configure Apache Arrow en un entorno agrupado distribuido
  • Use Apache Arrow para acceder a datos de fuentes de datos dispares
  • Use Apache Arrow para evitar la necesidad de construir y mantener tuberías complejas de ETL
  • Analice datos a través de fuentes de datos dispares sin tener que consolidarlos en un repositorio centralizado.
Audiencia
  • Científicos de datos
  • Ingenieros de datos
Formato del curso
  • Conferencia parcial, discusión parcial, ejercicios y práctica práctica.
Nota
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => [outline] =>

Introduction

  • Apache Arrow vs Parquet

Installing and Configuring Apache Arrow

Overview of Apache Arrow Features and Architecture

Exploring Data with Pandas and Apache Arrow

Exploring Data with Spark and Apache Arrow

Exploring Data with R and Apache Arrow

Exploring Data with MapD and Apache Arrow

Other Data Analysis Integrations

  • PySpark, Parquet files on S3, and Oracle tables and Elasticsearch indices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037432 [source_title] => Apache Arrow for Data Analysis across Disparate Data Sources [source_language] => en [cert_code] => [weight] => -976 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => arrow ) ) [1] => Array ( [0] => ec_4966 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => arrow [hr_nid] => 284282 [title] => Apache Arrow for Data Analysis across Disparate Data Sources [requirements] =>
  • A basic undersanding of SQL
  • Familiarity with Python or R
  • Some familiarity with Apache Spark
[overview] => Apache Arrow es un marco de procesamiento de datos en memoria de código abierto. A menudo se usa junto con otras herramientas de ciencia de datos para acceder a almacenes de datos dispares para su análisis. Se integra bien con otras tecnologías, como bases de datos de GPU , bibliotecas y herramientas de aprendizaje automático, motores de ejecución y marcos de visualización de datos. En esta capacitación en vivo dirigida por un instructor en el sitio, los participantes aprenderán cómo integrar Apache Arrow con varios marcos de Data Science para acceder a datos de fuentes de datos dispares. Al final de esta capacitación, los participantes podrán:
  • Instale y configure Apache Arrow en un entorno agrupado distribuido
  • Use Apache Arrow para acceder a datos de fuentes de datos dispares
  • Use Apache Arrow para evitar la necesidad de construir y mantener tuberías complejas de ETL
  • Analice datos a través de fuentes de datos dispares sin tener que consolidarlos en un repositorio centralizado.
Audiencia
  • Científicos de datos
  • Ingenieros de datos
Formato del curso
  • Conferencia parcial, discusión parcial, ejercicios y práctica práctica.
Nota
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => [outline] =>

Introduction

  • Apache Arrow vs Parquet

Installing and Configuring Apache Arrow

Overview of Apache Arrow Features and Architecture

Exploring Data with Pandas and Apache Arrow

Exploring Data with Spark and Apache Arrow

Exploring Data with R and Apache Arrow

Exploring Data with MapD and Apache Arrow

Other Data Analysis Integrations

  • PySpark, Parquet files on S3, and Oracle tables and Elasticsearch indices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037432 [source_title] => Apache Arrow for Data Analysis across Disparate Data Sources [source_language] => en [cert_code] => [weight] => -976 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => arrow ) ) [2] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => apache-arrow [1] => guayaquil ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /apache-arrow/cursos/guayaquil ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/apache-arrow/cursos/guayaquil Undefined array key "nobleprog_default_trainer_journey" /apps/nobleprog-website/includes/functions/course-prices.php:286 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 286 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "nobleprog_default_trainer_journey" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 286 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 5437 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => arrow [venue_id] => ec_15661447 [vfdc] => 200.00 [vadc] => 50.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => arrow ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [arrow] => stdClass Object ( [course_code] => arrow [hr_nid] => 284282 [title] => Apache Arrow for Data Analysis across Disparate Data Sources [requirements] =>
  • A basic undersanding of SQL
  • Familiarity with Python or R
  • Some familiarity with Apache Spark
[overview] => Apache Arrow es un marco de procesamiento de datos en memoria de código abierto. A menudo se usa junto con otras herramientas de ciencia de datos para acceder a almacenes de datos dispares para su análisis. Se integra bien con otras tecnologías, como bases de datos de GPU , bibliotecas y herramientas de aprendizaje automático, motores de ejecución y marcos de visualización de datos. En esta capacitación en vivo dirigida por un instructor en el sitio, los participantes aprenderán cómo integrar Apache Arrow con varios marcos de Data Science para acceder a datos de fuentes de datos dispares. Al final de esta capacitación, los participantes podrán:
  • Instale y configure Apache Arrow en un entorno agrupado distribuido
  • Use Apache Arrow para acceder a datos de fuentes de datos dispares
  • Use Apache Arrow para evitar la necesidad de construir y mantener tuberías complejas de ETL
  • Analice datos a través de fuentes de datos dispares sin tener que consolidarlos en un repositorio centralizado.
Audiencia
  • Científicos de datos
  • Ingenieros de datos
Formato del curso
  • Conferencia parcial, discusión parcial, ejercicios y práctica práctica.
Nota
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => [outline] =>

Introduction

  • Apache Arrow vs Parquet

Installing and Configuring Apache Arrow

Overview of Apache Arrow Features and Architecture

Exploring Data with Pandas and Apache Arrow

Exploring Data with Spark and Apache Arrow

Exploring Data with R and Apache Arrow

Exploring Data with MapD and Apache Arrow

Other Data Analysis Integrations

  • PySpark, Parquet files on S3, and Oracle tables and Elasticsearch indices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037432 [source_title] => Apache Arrow for Data Analysis across Disparate Data Sources [source_language] => en [cert_code] => [weight] => -976 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => arrow ) ) [1] => Array ( [0] => ec_4966 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => arrow [hr_nid] => 284282 [title] => Apache Arrow for Data Analysis across Disparate Data Sources [requirements] =>
  • A basic undersanding of SQL
  • Familiarity with Python or R
  • Some familiarity with Apache Spark
[overview] => Apache Arrow es un marco de procesamiento de datos en memoria de código abierto. A menudo se usa junto con otras herramientas de ciencia de datos para acceder a almacenes de datos dispares para su análisis. Se integra bien con otras tecnologías, como bases de datos de GPU , bibliotecas y herramientas de aprendizaje automático, motores de ejecución y marcos de visualización de datos. En esta capacitación en vivo dirigida por un instructor en el sitio, los participantes aprenderán cómo integrar Apache Arrow con varios marcos de Data Science para acceder a datos de fuentes de datos dispares. Al final de esta capacitación, los participantes podrán:
  • Instale y configure Apache Arrow en un entorno agrupado distribuido
  • Use Apache Arrow para acceder a datos de fuentes de datos dispares
  • Use Apache Arrow para evitar la necesidad de construir y mantener tuberías complejas de ETL
  • Analice datos a través de fuentes de datos dispares sin tener que consolidarlos en un repositorio centralizado.
Audiencia
  • Científicos de datos
  • Ingenieros de datos
Formato del curso
  • Conferencia parcial, discusión parcial, ejercicios y práctica práctica.
Nota
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => [outline] =>

Introduction

  • Apache Arrow vs Parquet

Installing and Configuring Apache Arrow

Overview of Apache Arrow Features and Architecture

Exploring Data with Pandas and Apache Arrow

Exploring Data with Spark and Apache Arrow

Exploring Data with R and Apache Arrow

Exploring Data with MapD and Apache Arrow

Other Data Analysis Integrations

  • PySpark, Parquet files on S3, and Oracle tables and Elasticsearch indices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037432 [source_title] => Apache Arrow for Data Analysis across Disparate Data Sources [source_language] => en [cert_code] => [weight] => -976 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => arrow ) ) [2] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => apache-arrow [1] => guayaquil ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /apache-arrow/cursos/guayaquil ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/apache-arrow/cursos/guayaquil Undefined array key "nobleprog_price_rounding" /apps/nobleprog-website/includes/functions/course-prices.php:289 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 289 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "nobleprog_price_rounding" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 289 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 5437 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => arrow [venue_id] => ec_15661447 [vfdc] => 200.00 [vadc] => 50.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => arrow ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [arrow] => stdClass Object ( [course_code] => arrow [hr_nid] => 284282 [title] => Apache Arrow for Data Analysis across Disparate Data Sources [requirements] =>
  • A basic undersanding of SQL
  • Familiarity with Python or R
  • Some familiarity with Apache Spark
[overview] => Apache Arrow es un marco de procesamiento de datos en memoria de código abierto. A menudo se usa junto con otras herramientas de ciencia de datos para acceder a almacenes de datos dispares para su análisis. Se integra bien con otras tecnologías, como bases de datos de GPU , bibliotecas y herramientas de aprendizaje automático, motores de ejecución y marcos de visualización de datos. En esta capacitación en vivo dirigida por un instructor en el sitio, los participantes aprenderán cómo integrar Apache Arrow con varios marcos de Data Science para acceder a datos de fuentes de datos dispares. Al final de esta capacitación, los participantes podrán:
  • Instale y configure Apache Arrow en un entorno agrupado distribuido
  • Use Apache Arrow para acceder a datos de fuentes de datos dispares
  • Use Apache Arrow para evitar la necesidad de construir y mantener tuberías complejas de ETL
  • Analice datos a través de fuentes de datos dispares sin tener que consolidarlos en un repositorio centralizado.
Audiencia
  • Científicos de datos
  • Ingenieros de datos
Formato del curso
  • Conferencia parcial, discusión parcial, ejercicios y práctica práctica.
Nota
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => [outline] =>

Introduction

  • Apache Arrow vs Parquet

Installing and Configuring Apache Arrow

Overview of Apache Arrow Features and Architecture

Exploring Data with Pandas and Apache Arrow

Exploring Data with Spark and Apache Arrow

Exploring Data with R and Apache Arrow

Exploring Data with MapD and Apache Arrow

Other Data Analysis Integrations

  • PySpark, Parquet files on S3, and Oracle tables and Elasticsearch indices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037432 [source_title] => Apache Arrow for Data Analysis across Disparate Data Sources [source_language] => en [cert_code] => [weight] => -976 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => arrow ) ) [1] => Array ( [0] => ec_4966 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => arrow [hr_nid] => 284282 [title] => Apache Arrow for Data Analysis across Disparate Data Sources [requirements] =>
  • A basic undersanding of SQL
  • Familiarity with Python or R
  • Some familiarity with Apache Spark
[overview] => Apache Arrow es un marco de procesamiento de datos en memoria de código abierto. A menudo se usa junto con otras herramientas de ciencia de datos para acceder a almacenes de datos dispares para su análisis. Se integra bien con otras tecnologías, como bases de datos de GPU , bibliotecas y herramientas de aprendizaje automático, motores de ejecución y marcos de visualización de datos. En esta capacitación en vivo dirigida por un instructor en el sitio, los participantes aprenderán cómo integrar Apache Arrow con varios marcos de Data Science para acceder a datos de fuentes de datos dispares. Al final de esta capacitación, los participantes podrán:
  • Instale y configure Apache Arrow en un entorno agrupado distribuido
  • Use Apache Arrow para acceder a datos de fuentes de datos dispares
  • Use Apache Arrow para evitar la necesidad de construir y mantener tuberías complejas de ETL
  • Analice datos a través de fuentes de datos dispares sin tener que consolidarlos en un repositorio centralizado.
Audiencia
  • Científicos de datos
  • Ingenieros de datos
Formato del curso
  • Conferencia parcial, discusión parcial, ejercicios y práctica práctica.
Nota
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => [outline] =>

Introduction

  • Apache Arrow vs Parquet

Installing and Configuring Apache Arrow

Overview of Apache Arrow Features and Architecture

Exploring Data with Pandas and Apache Arrow

Exploring Data with Spark and Apache Arrow

Exploring Data with R and Apache Arrow

Exploring Data with MapD and Apache Arrow

Other Data Analysis Integrations

  • PySpark, Parquet files on S3, and Oracle tables and Elasticsearch indices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037432 [source_title] => Apache Arrow for Data Analysis across Disparate Data Sources [source_language] => en [cert_code] => [weight] => -976 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => arrow ) ) [2] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => apache-arrow [1] => guayaquil ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /apache-arrow/cursos/guayaquil ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/apache-arrow/cursos/guayaquil Undefined array key "sdp" /apps/nobleprog-website/includes/functions/course-prices.php:281 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 281 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "sdp" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 281 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 5437 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => arrow [venue_id] => ec_15661446 [vfdc] => 175.00 [vadc] => 60.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => arrow ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [arrow] => stdClass Object ( [course_code] => arrow [hr_nid] => 284282 [title] => Apache Arrow for Data Analysis across Disparate Data Sources [requirements] =>
  • A basic undersanding of SQL
  • Familiarity with Python or R
  • Some familiarity with Apache Spark
[overview] => Apache Arrow es un marco de procesamiento de datos en memoria de código abierto. A menudo se usa junto con otras herramientas de ciencia de datos para acceder a almacenes de datos dispares para su análisis. Se integra bien con otras tecnologías, como bases de datos de GPU , bibliotecas y herramientas de aprendizaje automático, motores de ejecución y marcos de visualización de datos. En esta capacitación en vivo dirigida por un instructor en el sitio, los participantes aprenderán cómo integrar Apache Arrow con varios marcos de Data Science para acceder a datos de fuentes de datos dispares. Al final de esta capacitación, los participantes podrán:
  • Instale y configure Apache Arrow en un entorno agrupado distribuido
  • Use Apache Arrow para acceder a datos de fuentes de datos dispares
  • Use Apache Arrow para evitar la necesidad de construir y mantener tuberías complejas de ETL
  • Analice datos a través de fuentes de datos dispares sin tener que consolidarlos en un repositorio centralizado.
Audiencia
  • Científicos de datos
  • Ingenieros de datos
Formato del curso
  • Conferencia parcial, discusión parcial, ejercicios y práctica práctica.
Nota
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => [outline] =>

Introduction

  • Apache Arrow vs Parquet

Installing and Configuring Apache Arrow

Overview of Apache Arrow Features and Architecture

Exploring Data with Pandas and Apache Arrow

Exploring Data with Spark and Apache Arrow

Exploring Data with R and Apache Arrow

Exploring Data with MapD and Apache Arrow

Other Data Analysis Integrations

  • PySpark, Parquet files on S3, and Oracle tables and Elasticsearch indices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037432 [source_title] => Apache Arrow for Data Analysis across Disparate Data Sources [source_language] => en [cert_code] => [weight] => -976 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => arrow ) ) [1] => Array ( [0] => ec_4966 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => arrow [hr_nid] => 284282 [title] => Apache Arrow for Data Analysis across Disparate Data Sources [requirements] =>
  • A basic undersanding of SQL
  • Familiarity with Python or R
  • Some familiarity with Apache Spark
[overview] => Apache Arrow es un marco de procesamiento de datos en memoria de código abierto. A menudo se usa junto con otras herramientas de ciencia de datos para acceder a almacenes de datos dispares para su análisis. Se integra bien con otras tecnologías, como bases de datos de GPU , bibliotecas y herramientas de aprendizaje automático, motores de ejecución y marcos de visualización de datos. En esta capacitación en vivo dirigida por un instructor en el sitio, los participantes aprenderán cómo integrar Apache Arrow con varios marcos de Data Science para acceder a datos de fuentes de datos dispares. Al final de esta capacitación, los participantes podrán:
  • Instale y configure Apache Arrow en un entorno agrupado distribuido
  • Use Apache Arrow para acceder a datos de fuentes de datos dispares
  • Use Apache Arrow para evitar la necesidad de construir y mantener tuberías complejas de ETL
  • Analice datos a través de fuentes de datos dispares sin tener que consolidarlos en un repositorio centralizado.
Audiencia
  • Científicos de datos
  • Ingenieros de datos
Formato del curso
  • Conferencia parcial, discusión parcial, ejercicios y práctica práctica.
Nota
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => [outline] =>

Introduction

  • Apache Arrow vs Parquet

Installing and Configuring Apache Arrow

Overview of Apache Arrow Features and Architecture

Exploring Data with Pandas and Apache Arrow

Exploring Data with Spark and Apache Arrow

Exploring Data with R and Apache Arrow

Exploring Data with MapD and Apache Arrow

Other Data Analysis Integrations

  • PySpark, Parquet files on S3, and Oracle tables and Elasticsearch indices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037432 [source_title] => Apache Arrow for Data Analysis across Disparate Data Sources [source_language] => en [cert_code] => [weight] => -976 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => arrow ) ) [2] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => apache-arrow [1] => guayaquil ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /apache-arrow/cursos/guayaquil ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/apache-arrow/cursos/guayaquil Undefined array key "nobleprog_default_trainer_journey" /apps/nobleprog-website/includes/functions/course-prices.php:286 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 286 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "nobleprog_default_trainer_journey" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 286 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 5437 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => arrow [venue_id] => ec_15661446 [vfdc] => 175.00 [vadc] => 60.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => arrow ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [arrow] => stdClass Object ( [course_code] => arrow [hr_nid] => 284282 [title] => Apache Arrow for Data Analysis across Disparate Data Sources [requirements] =>
  • A basic undersanding of SQL
  • Familiarity with Python or R
  • Some familiarity with Apache Spark
[overview] => Apache Arrow es un marco de procesamiento de datos en memoria de código abierto. A menudo se usa junto con otras herramientas de ciencia de datos para acceder a almacenes de datos dispares para su análisis. Se integra bien con otras tecnologías, como bases de datos de GPU , bibliotecas y herramientas de aprendizaje automático, motores de ejecución y marcos de visualización de datos. En esta capacitación en vivo dirigida por un instructor en el sitio, los participantes aprenderán cómo integrar Apache Arrow con varios marcos de Data Science para acceder a datos de fuentes de datos dispares. Al final de esta capacitación, los participantes podrán:
  • Instale y configure Apache Arrow en un entorno agrupado distribuido
  • Use Apache Arrow para acceder a datos de fuentes de datos dispares
  • Use Apache Arrow para evitar la necesidad de construir y mantener tuberías complejas de ETL
  • Analice datos a través de fuentes de datos dispares sin tener que consolidarlos en un repositorio centralizado.
Audiencia
  • Científicos de datos
  • Ingenieros de datos
Formato del curso
  • Conferencia parcial, discusión parcial, ejercicios y práctica práctica.
Nota
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => [outline] =>

Introduction

  • Apache Arrow vs Parquet

Installing and Configuring Apache Arrow

Overview of Apache Arrow Features and Architecture

Exploring Data with Pandas and Apache Arrow

Exploring Data with Spark and Apache Arrow

Exploring Data with R and Apache Arrow

Exploring Data with MapD and Apache Arrow

Other Data Analysis Integrations

  • PySpark, Parquet files on S3, and Oracle tables and Elasticsearch indices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037432 [source_title] => Apache Arrow for Data Analysis across Disparate Data Sources [source_language] => en [cert_code] => [weight] => -976 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => arrow ) ) [1] => Array ( [0] => ec_4966 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => arrow [hr_nid] => 284282 [title] => Apache Arrow for Data Analysis across Disparate Data Sources [requirements] =>
  • A basic undersanding of SQL
  • Familiarity with Python or R
  • Some familiarity with Apache Spark
[overview] => Apache Arrow es un marco de procesamiento de datos en memoria de código abierto. A menudo se usa junto con otras herramientas de ciencia de datos para acceder a almacenes de datos dispares para su análisis. Se integra bien con otras tecnologías, como bases de datos de GPU , bibliotecas y herramientas de aprendizaje automático, motores de ejecución y marcos de visualización de datos. En esta capacitación en vivo dirigida por un instructor en el sitio, los participantes aprenderán cómo integrar Apache Arrow con varios marcos de Data Science para acceder a datos de fuentes de datos dispares. Al final de esta capacitación, los participantes podrán:
  • Instale y configure Apache Arrow en un entorno agrupado distribuido
  • Use Apache Arrow para acceder a datos de fuentes de datos dispares
  • Use Apache Arrow para evitar la necesidad de construir y mantener tuberías complejas de ETL
  • Analice datos a través de fuentes de datos dispares sin tener que consolidarlos en un repositorio centralizado.
Audiencia
  • Científicos de datos
  • Ingenieros de datos
Formato del curso
  • Conferencia parcial, discusión parcial, ejercicios y práctica práctica.
Nota
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => [outline] =>

Introduction

  • Apache Arrow vs Parquet

Installing and Configuring Apache Arrow

Overview of Apache Arrow Features and Architecture

Exploring Data with Pandas and Apache Arrow

Exploring Data with Spark and Apache Arrow

Exploring Data with R and Apache Arrow

Exploring Data with MapD and Apache Arrow

Other Data Analysis Integrations

  • PySpark, Parquet files on S3, and Oracle tables and Elasticsearch indices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037432 [source_title] => Apache Arrow for Data Analysis across Disparate Data Sources [source_language] => en [cert_code] => [weight] => -976 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => arrow ) ) [2] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => apache-arrow [1] => guayaquil ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /apache-arrow/cursos/guayaquil ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/apache-arrow/cursos/guayaquil Undefined array key "nobleprog_price_rounding" /apps/nobleprog-website/includes/functions/course-prices.php:289 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 289 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "nobleprog_price_rounding" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 289 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 5437 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => arrow [venue_id] => ec_15661446 [vfdc] => 175.00 [vadc] => 60.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => arrow ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [arrow] => stdClass Object ( [course_code] => arrow [hr_nid] => 284282 [title] => Apache Arrow for Data Analysis across Disparate Data Sources [requirements] =>
  • A basic undersanding of SQL
  • Familiarity with Python or R
  • Some familiarity with Apache Spark
[overview] => Apache Arrow es un marco de procesamiento de datos en memoria de código abierto. A menudo se usa junto con otras herramientas de ciencia de datos para acceder a almacenes de datos dispares para su análisis. Se integra bien con otras tecnologías, como bases de datos de GPU , bibliotecas y herramientas de aprendizaje automático, motores de ejecución y marcos de visualización de datos. En esta capacitación en vivo dirigida por un instructor en el sitio, los participantes aprenderán cómo integrar Apache Arrow con varios marcos de Data Science para acceder a datos de fuentes de datos dispares. Al final de esta capacitación, los participantes podrán:
  • Instale y configure Apache Arrow en un entorno agrupado distribuido
  • Use Apache Arrow para acceder a datos de fuentes de datos dispares
  • Use Apache Arrow para evitar la necesidad de construir y mantener tuberías complejas de ETL
  • Analice datos a través de fuentes de datos dispares sin tener que consolidarlos en un repositorio centralizado.
Audiencia
  • Científicos de datos
  • Ingenieros de datos
Formato del curso
  • Conferencia parcial, discusión parcial, ejercicios y práctica práctica.
Nota
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => [outline] =>

Introduction

  • Apache Arrow vs Parquet

Installing and Configuring Apache Arrow

Overview of Apache Arrow Features and Architecture

Exploring Data with Pandas and Apache Arrow

Exploring Data with Spark and Apache Arrow

Exploring Data with R and Apache Arrow

Exploring Data with MapD and Apache Arrow

Other Data Analysis Integrations

  • PySpark, Parquet files on S3, and Oracle tables and Elasticsearch indices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037432 [source_title] => Apache Arrow for Data Analysis across Disparate Data Sources [source_language] => en [cert_code] => [weight] => -976 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => arrow ) ) [1] => Array ( [0] => ec_4966 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => arrow [hr_nid] => 284282 [title] => Apache Arrow for Data Analysis across Disparate Data Sources [requirements] =>
  • A basic undersanding of SQL
  • Familiarity with Python or R
  • Some familiarity with Apache Spark
[overview] => Apache Arrow es un marco de procesamiento de datos en memoria de código abierto. A menudo se usa junto con otras herramientas de ciencia de datos para acceder a almacenes de datos dispares para su análisis. Se integra bien con otras tecnologías, como bases de datos de GPU , bibliotecas y herramientas de aprendizaje automático, motores de ejecución y marcos de visualización de datos. En esta capacitación en vivo dirigida por un instructor en el sitio, los participantes aprenderán cómo integrar Apache Arrow con varios marcos de Data Science para acceder a datos de fuentes de datos dispares. Al final de esta capacitación, los participantes podrán:
  • Instale y configure Apache Arrow en un entorno agrupado distribuido
  • Use Apache Arrow para acceder a datos de fuentes de datos dispares
  • Use Apache Arrow para evitar la necesidad de construir y mantener tuberías complejas de ETL
  • Analice datos a través de fuentes de datos dispares sin tener que consolidarlos en un repositorio centralizado.
Audiencia
  • Científicos de datos
  • Ingenieros de datos
Formato del curso
  • Conferencia parcial, discusión parcial, ejercicios y práctica práctica.
Nota
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => [outline] =>

Introduction

  • Apache Arrow vs Parquet

Installing and Configuring Apache Arrow

Overview of Apache Arrow Features and Architecture

Exploring Data with Pandas and Apache Arrow

Exploring Data with Spark and Apache Arrow

Exploring Data with R and Apache Arrow

Exploring Data with MapD and Apache Arrow

Other Data Analysis Integrations

  • PySpark, Parquet files on S3, and Oracle tables and Elasticsearch indices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037432 [source_title] => Apache Arrow for Data Analysis across Disparate Data Sources [source_language] => en [cert_code] => [weight] => -976 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => arrow ) ) [2] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => apache-arrow [1] => guayaquil ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /apache-arrow/cursos/guayaquil ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/apache-arrow/cursos/guayaquil Undefined array key "sdp" /apps/nobleprog-website/includes/functions/course-prices.php:281 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 281 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "sdp" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 281 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 5437 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => arrow [venue_id] => ec_15661447 [vfdc] => 200.00 [vadc] => 50.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => arrow ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [arrow] => stdClass Object ( [course_code] => arrow [hr_nid] => 284282 [title] => Apache Arrow for Data Analysis across Disparate Data Sources [requirements] =>
  • A basic undersanding of SQL
  • Familiarity with Python or R
  • Some familiarity with Apache Spark
[overview] => Apache Arrow es un marco de procesamiento de datos en memoria de código abierto. A menudo se usa junto con otras herramientas de ciencia de datos para acceder a almacenes de datos dispares para su análisis. Se integra bien con otras tecnologías, como bases de datos de GPU , bibliotecas y herramientas de aprendizaje automático, motores de ejecución y marcos de visualización de datos. En esta capacitación en vivo dirigida por un instructor en el sitio, los participantes aprenderán cómo integrar Apache Arrow con varios marcos de Data Science para acceder a datos de fuentes de datos dispares. Al final de esta capacitación, los participantes podrán:
  • Instale y configure Apache Arrow en un entorno agrupado distribuido
  • Use Apache Arrow para acceder a datos de fuentes de datos dispares
  • Use Apache Arrow para evitar la necesidad de construir y mantener tuberías complejas de ETL
  • Analice datos a través de fuentes de datos dispares sin tener que consolidarlos en un repositorio centralizado.
Audiencia
  • Científicos de datos
  • Ingenieros de datos
Formato del curso
  • Conferencia parcial, discusión parcial, ejercicios y práctica práctica.
Nota
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => [outline] =>

Introduction

  • Apache Arrow vs Parquet

Installing and Configuring Apache Arrow

Overview of Apache Arrow Features and Architecture

Exploring Data with Pandas and Apache Arrow

Exploring Data with Spark and Apache Arrow

Exploring Data with R and Apache Arrow

Exploring Data with MapD and Apache Arrow

Other Data Analysis Integrations

  • PySpark, Parquet files on S3, and Oracle tables and Elasticsearch indices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037432 [source_title] => Apache Arrow for Data Analysis across Disparate Data Sources [source_language] => en [cert_code] => [weight] => -976 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => arrow ) ) [1] => Array ( [0] => ec_4966 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => arrow [hr_nid] => 284282 [title] => Apache Arrow for Data Analysis across Disparate Data Sources [requirements] =>
  • A basic undersanding of SQL
  • Familiarity with Python or R
  • Some familiarity with Apache Spark
[overview] => Apache Arrow es un marco de procesamiento de datos en memoria de código abierto. A menudo se usa junto con otras herramientas de ciencia de datos para acceder a almacenes de datos dispares para su análisis. Se integra bien con otras tecnologías, como bases de datos de GPU , bibliotecas y herramientas de aprendizaje automático, motores de ejecución y marcos de visualización de datos. En esta capacitación en vivo dirigida por un instructor en el sitio, los participantes aprenderán cómo integrar Apache Arrow con varios marcos de Data Science para acceder a datos de fuentes de datos dispares. Al final de esta capacitación, los participantes podrán:
  • Instale y configure Apache Arrow en un entorno agrupado distribuido
  • Use Apache Arrow para acceder a datos de fuentes de datos dispares
  • Use Apache Arrow para evitar la necesidad de construir y mantener tuberías complejas de ETL
  • Analice datos a través de fuentes de datos dispares sin tener que consolidarlos en un repositorio centralizado.
Audiencia
  • Científicos de datos
  • Ingenieros de datos
Formato del curso
  • Conferencia parcial, discusión parcial, ejercicios y práctica práctica.
Nota
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => [outline] =>

Introduction

  • Apache Arrow vs Parquet

Installing and Configuring Apache Arrow

Overview of Apache Arrow Features and Architecture

Exploring Data with Pandas and Apache Arrow

Exploring Data with Spark and Apache Arrow

Exploring Data with R and Apache Arrow

Exploring Data with MapD and Apache Arrow

Other Data Analysis Integrations

  • PySpark, Parquet files on S3, and Oracle tables and Elasticsearch indices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037432 [source_title] => Apache Arrow for Data Analysis across Disparate Data Sources [source_language] => en [cert_code] => [weight] => -976 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => arrow ) ) [2] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => apache-arrow [1] => guayaquil ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /apache-arrow/cursos/guayaquil ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/apache-arrow/cursos/guayaquil Undefined array key "nobleprog_default_trainer_journey" /apps/nobleprog-website/includes/functions/course-prices.php:286 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 286 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "nobleprog_default_trainer_journey" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 286 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 5437 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => arrow [venue_id] => ec_15661447 [vfdc] => 200.00 [vadc] => 50.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => arrow ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [arrow] => stdClass Object ( [course_code] => arrow [hr_nid] => 284282 [title] => Apache Arrow for Data Analysis across Disparate Data Sources [requirements] =>
  • A basic undersanding of SQL
  • Familiarity with Python or R
  • Some familiarity with Apache Spark
[overview] => Apache Arrow es un marco de procesamiento de datos en memoria de código abierto. A menudo se usa junto con otras herramientas de ciencia de datos para acceder a almacenes de datos dispares para su análisis. Se integra bien con otras tecnologías, como bases de datos de GPU , bibliotecas y herramientas de aprendizaje automático, motores de ejecución y marcos de visualización de datos. En esta capacitación en vivo dirigida por un instructor en el sitio, los participantes aprenderán cómo integrar Apache Arrow con varios marcos de Data Science para acceder a datos de fuentes de datos dispares. Al final de esta capacitación, los participantes podrán:
  • Instale y configure Apache Arrow en un entorno agrupado distribuido
  • Use Apache Arrow para acceder a datos de fuentes de datos dispares
  • Use Apache Arrow para evitar la necesidad de construir y mantener tuberías complejas de ETL
  • Analice datos a través de fuentes de datos dispares sin tener que consolidarlos en un repositorio centralizado.
Audiencia
  • Científicos de datos
  • Ingenieros de datos
Formato del curso
  • Conferencia parcial, discusión parcial, ejercicios y práctica práctica.
Nota
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => [outline] =>

Introduction

  • Apache Arrow vs Parquet

Installing and Configuring Apache Arrow

Overview of Apache Arrow Features and Architecture

Exploring Data with Pandas and Apache Arrow

Exploring Data with Spark and Apache Arrow

Exploring Data with R and Apache Arrow

Exploring Data with MapD and Apache Arrow

Other Data Analysis Integrations

  • PySpark, Parquet files on S3, and Oracle tables and Elasticsearch indices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037432 [source_title] => Apache Arrow for Data Analysis across Disparate Data Sources [source_language] => en [cert_code] => [weight] => -976 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => arrow ) ) [1] => Array ( [0] => ec_4966 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => arrow [hr_nid] => 284282 [title] => Apache Arrow for Data Analysis across Disparate Data Sources [requirements] =>
  • A basic undersanding of SQL
  • Familiarity with Python or R
  • Some familiarity with Apache Spark
[overview] => Apache Arrow es un marco de procesamiento de datos en memoria de código abierto. A menudo se usa junto con otras herramientas de ciencia de datos para acceder a almacenes de datos dispares para su análisis. Se integra bien con otras tecnologías, como bases de datos de GPU , bibliotecas y herramientas de aprendizaje automático, motores de ejecución y marcos de visualización de datos. En esta capacitación en vivo dirigida por un instructor en el sitio, los participantes aprenderán cómo integrar Apache Arrow con varios marcos de Data Science para acceder a datos de fuentes de datos dispares. Al final de esta capacitación, los participantes podrán:
  • Instale y configure Apache Arrow en un entorno agrupado distribuido
  • Use Apache Arrow para acceder a datos de fuentes de datos dispares
  • Use Apache Arrow para evitar la necesidad de construir y mantener tuberías complejas de ETL
  • Analice datos a través de fuentes de datos dispares sin tener que consolidarlos en un repositorio centralizado.
Audiencia
  • Científicos de datos
  • Ingenieros de datos
Formato del curso
  • Conferencia parcial, discusión parcial, ejercicios y práctica práctica.
Nota
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => [outline] =>

Introduction

  • Apache Arrow vs Parquet

Installing and Configuring Apache Arrow

Overview of Apache Arrow Features and Architecture

Exploring Data with Pandas and Apache Arrow

Exploring Data with Spark and Apache Arrow

Exploring Data with R and Apache Arrow

Exploring Data with MapD and Apache Arrow

Other Data Analysis Integrations

  • PySpark, Parquet files on S3, and Oracle tables and Elasticsearch indices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037432 [source_title] => Apache Arrow for Data Analysis across Disparate Data Sources [source_language] => en [cert_code] => [weight] => -976 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => arrow ) ) [2] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => apache-arrow [1] => guayaquil ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /apache-arrow/cursos/guayaquil ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/apache-arrow/cursos/guayaquil Undefined array key "nobleprog_price_rounding" /apps/nobleprog-website/includes/functions/course-prices.php:289 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 289 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "nobleprog_price_rounding" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 289 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 5437 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => arrow [venue_id] => ec_15661447 [vfdc] => 200.00 [vadc] => 50.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => arrow ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [arrow] => stdClass Object ( [course_code] => arrow [hr_nid] => 284282 [title] => Apache Arrow for Data Analysis across Disparate Data Sources [requirements] =>
  • A basic undersanding of SQL
  • Familiarity with Python or R
  • Some familiarity with Apache Spark
[overview] => Apache Arrow es un marco de procesamiento de datos en memoria de código abierto. A menudo se usa junto con otras herramientas de ciencia de datos para acceder a almacenes de datos dispares para su análisis. Se integra bien con otras tecnologías, como bases de datos de GPU , bibliotecas y herramientas de aprendizaje automático, motores de ejecución y marcos de visualización de datos. En esta capacitación en vivo dirigida por un instructor en el sitio, los participantes aprenderán cómo integrar Apache Arrow con varios marcos de Data Science para acceder a datos de fuentes de datos dispares. Al final de esta capacitación, los participantes podrán:
  • Instale y configure Apache Arrow en un entorno agrupado distribuido
  • Use Apache Arrow para acceder a datos de fuentes de datos dispares
  • Use Apache Arrow para evitar la necesidad de construir y mantener tuberías complejas de ETL
  • Analice datos a través de fuentes de datos dispares sin tener que consolidarlos en un repositorio centralizado.
Audiencia
  • Científicos de datos
  • Ingenieros de datos
Formato del curso
  • Conferencia parcial, discusión parcial, ejercicios y práctica práctica.
Nota
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => [outline] =>

Introduction

  • Apache Arrow vs Parquet

Installing and Configuring Apache Arrow

Overview of Apache Arrow Features and Architecture

Exploring Data with Pandas and Apache Arrow

Exploring Data with Spark and Apache Arrow

Exploring Data with R and Apache Arrow

Exploring Data with MapD and Apache Arrow

Other Data Analysis Integrations

  • PySpark, Parquet files on S3, and Oracle tables and Elasticsearch indices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037432 [source_title] => Apache Arrow for Data Analysis across Disparate Data Sources [source_language] => en [cert_code] => [weight] => -976 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => arrow ) ) [1] => Array ( [0] => ec_4966 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => arrow [hr_nid] => 284282 [title] => Apache Arrow for Data Analysis across Disparate Data Sources [requirements] =>
  • A basic undersanding of SQL
  • Familiarity with Python or R
  • Some familiarity with Apache Spark
[overview] => Apache Arrow es un marco de procesamiento de datos en memoria de código abierto. A menudo se usa junto con otras herramientas de ciencia de datos para acceder a almacenes de datos dispares para su análisis. Se integra bien con otras tecnologías, como bases de datos de GPU , bibliotecas y herramientas de aprendizaje automático, motores de ejecución y marcos de visualización de datos. En esta capacitación en vivo dirigida por un instructor en el sitio, los participantes aprenderán cómo integrar Apache Arrow con varios marcos de Data Science para acceder a datos de fuentes de datos dispares. Al final de esta capacitación, los participantes podrán:
  • Instale y configure Apache Arrow en un entorno agrupado distribuido
  • Use Apache Arrow para acceder a datos de fuentes de datos dispares
  • Use Apache Arrow para evitar la necesidad de construir y mantener tuberías complejas de ETL
  • Analice datos a través de fuentes de datos dispares sin tener que consolidarlos en un repositorio centralizado.
Audiencia
  • Científicos de datos
  • Ingenieros de datos
Formato del curso
  • Conferencia parcial, discusión parcial, ejercicios y práctica práctica.
Nota
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => [outline] =>

Introduction

  • Apache Arrow vs Parquet

Installing and Configuring Apache Arrow

Overview of Apache Arrow Features and Architecture

Exploring Data with Pandas and Apache Arrow

Exploring Data with Spark and Apache Arrow

Exploring Data with R and Apache Arrow

Exploring Data with MapD and Apache Arrow

Other Data Analysis Integrations

  • PySpark, Parquet files on S3, and Oracle tables and Elasticsearch indices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037432 [source_title] => Apache Arrow for Data Analysis across Disparate Data Sources [source_language] => en [cert_code] => [weight] => -976 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => arrow ) ) [2] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => apache-arrow [1] => guayaquil ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /apache-arrow/cursos/guayaquil ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/apache-arrow/cursos/guayaquil Undefined array key "sdp" /apps/nobleprog-website/includes/functions/course-prices.php:281 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 281 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "sdp" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 281 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 5437 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => arrow [venue_id] => ec_15661446 [vfdc] => 175.00 [vadc] => 60.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => arrow ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [arrow] => stdClass Object ( [course_code] => arrow [hr_nid] => 284282 [title] => Apache Arrow for Data Analysis across Disparate Data Sources [requirements] =>
  • A basic undersanding of SQL
  • Familiarity with Python or R
  • Some familiarity with Apache Spark
[overview] => Apache Arrow es un marco de procesamiento de datos en memoria de código abierto. A menudo se usa junto con otras herramientas de ciencia de datos para acceder a almacenes de datos dispares para su análisis. Se integra bien con otras tecnologías, como bases de datos de GPU , bibliotecas y herramientas de aprendizaje automático, motores de ejecución y marcos de visualización de datos. En esta capacitación en vivo dirigida por un instructor en el sitio, los participantes aprenderán cómo integrar Apache Arrow con varios marcos de Data Science para acceder a datos de fuentes de datos dispares. Al final de esta capacitación, los participantes podrán:
  • Instale y configure Apache Arrow en un entorno agrupado distribuido
  • Use Apache Arrow para acceder a datos de fuentes de datos dispares
  • Use Apache Arrow para evitar la necesidad de construir y mantener tuberías complejas de ETL
  • Analice datos a través de fuentes de datos dispares sin tener que consolidarlos en un repositorio centralizado.
Audiencia
  • Científicos de datos
  • Ingenieros de datos
Formato del curso
  • Conferencia parcial, discusión parcial, ejercicios y práctica práctica.
Nota
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => [outline] =>

Introduction

  • Apache Arrow vs Parquet

Installing and Configuring Apache Arrow

Overview of Apache Arrow Features and Architecture

Exploring Data with Pandas and Apache Arrow

Exploring Data with Spark and Apache Arrow

Exploring Data with R and Apache Arrow

Exploring Data with MapD and Apache Arrow

Other Data Analysis Integrations

  • PySpark, Parquet files on S3, and Oracle tables and Elasticsearch indices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037432 [source_title] => Apache Arrow for Data Analysis across Disparate Data Sources [source_language] => en [cert_code] => [weight] => -976 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => arrow ) ) [1] => Array ( [0] => ec_4966 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => arrow [hr_nid] => 284282 [title] => Apache Arrow for Data Analysis across Disparate Data Sources [requirements] =>
  • A basic undersanding of SQL
  • Familiarity with Python or R
  • Some familiarity with Apache Spark
[overview] => Apache Arrow es un marco de procesamiento de datos en memoria de código abierto. A menudo se usa junto con otras herramientas de ciencia de datos para acceder a almacenes de datos dispares para su análisis. Se integra bien con otras tecnologías, como bases de datos de GPU , bibliotecas y herramientas de aprendizaje automático, motores de ejecución y marcos de visualización de datos. En esta capacitación en vivo dirigida por un instructor en el sitio, los participantes aprenderán cómo integrar Apache Arrow con varios marcos de Data Science para acceder a datos de fuentes de datos dispares. Al final de esta capacitación, los participantes podrán:
  • Instale y configure Apache Arrow en un entorno agrupado distribuido
  • Use Apache Arrow para acceder a datos de fuentes de datos dispares
  • Use Apache Arrow para evitar la necesidad de construir y mantener tuberías complejas de ETL
  • Analice datos a través de fuentes de datos dispares sin tener que consolidarlos en un repositorio centralizado.
Audiencia
  • Científicos de datos
  • Ingenieros de datos
Formato del curso
  • Conferencia parcial, discusión parcial, ejercicios y práctica práctica.
Nota
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => [outline] =>

Introduction

  • Apache Arrow vs Parquet

Installing and Configuring Apache Arrow

Overview of Apache Arrow Features and Architecture

Exploring Data with Pandas and Apache Arrow

Exploring Data with Spark and Apache Arrow

Exploring Data with R and Apache Arrow

Exploring Data with MapD and Apache Arrow

Other Data Analysis Integrations

  • PySpark, Parquet files on S3, and Oracle tables and Elasticsearch indices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037432 [source_title] => Apache Arrow for Data Analysis across Disparate Data Sources [source_language] => en [cert_code] => [weight] => -976 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => arrow ) ) [2] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => apache-arrow [1] => guayaquil ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /apache-arrow/cursos/guayaquil ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/apache-arrow/cursos/guayaquil Undefined array key "nobleprog_default_trainer_journey" /apps/nobleprog-website/includes/functions/course-prices.php:286 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 286 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "nobleprog_default_trainer_journey" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 286 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 5437 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => arrow [venue_id] => ec_15661446 [vfdc] => 175.00 [vadc] => 60.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => arrow ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [arrow] => stdClass Object ( [course_code] => arrow [hr_nid] => 284282 [title] => Apache Arrow for Data Analysis across Disparate Data Sources [requirements] =>
  • A basic undersanding of SQL
  • Familiarity with Python or R
  • Some familiarity with Apache Spark
[overview] => Apache Arrow es un marco de procesamiento de datos en memoria de código abierto. A menudo se usa junto con otras herramientas de ciencia de datos para acceder a almacenes de datos dispares para su análisis. Se integra bien con otras tecnologías, como bases de datos de GPU , bibliotecas y herramientas de aprendizaje automático, motores de ejecución y marcos de visualización de datos. En esta capacitación en vivo dirigida por un instructor en el sitio, los participantes aprenderán cómo integrar Apache Arrow con varios marcos de Data Science para acceder a datos de fuentes de datos dispares. Al final de esta capacitación, los participantes podrán:
  • Instale y configure Apache Arrow en un entorno agrupado distribuido
  • Use Apache Arrow para acceder a datos de fuentes de datos dispares
  • Use Apache Arrow para evitar la necesidad de construir y mantener tuberías complejas de ETL
  • Analice datos a través de fuentes de datos dispares sin tener que consolidarlos en un repositorio centralizado.
Audiencia
  • Científicos de datos
  • Ingenieros de datos
Formato del curso
  • Conferencia parcial, discusión parcial, ejercicios y práctica práctica.
Nota
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => [outline] =>

Introduction

  • Apache Arrow vs Parquet

Installing and Configuring Apache Arrow

Overview of Apache Arrow Features and Architecture

Exploring Data with Pandas and Apache Arrow

Exploring Data with Spark and Apache Arrow

Exploring Data with R and Apache Arrow

Exploring Data with MapD and Apache Arrow

Other Data Analysis Integrations

  • PySpark, Parquet files on S3, and Oracle tables and Elasticsearch indices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037432 [source_title] => Apache Arrow for Data Analysis across Disparate Data Sources [source_language] => en [cert_code] => [weight] => -976 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => arrow ) ) [1] => Array ( [0] => ec_4966 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => arrow [hr_nid] => 284282 [title] => Apache Arrow for Data Analysis across Disparate Data Sources [requirements] =>
  • A basic undersanding of SQL
  • Familiarity with Python or R
  • Some familiarity with Apache Spark
[overview] => Apache Arrow es un marco de procesamiento de datos en memoria de código abierto. A menudo se usa junto con otras herramientas de ciencia de datos para acceder a almacenes de datos dispares para su análisis. Se integra bien con otras tecnologías, como bases de datos de GPU , bibliotecas y herramientas de aprendizaje automático, motores de ejecución y marcos de visualización de datos. En esta capacitación en vivo dirigida por un instructor en el sitio, los participantes aprenderán cómo integrar Apache Arrow con varios marcos de Data Science para acceder a datos de fuentes de datos dispares. Al final de esta capacitación, los participantes podrán:
  • Instale y configure Apache Arrow en un entorno agrupado distribuido
  • Use Apache Arrow para acceder a datos de fuentes de datos dispares
  • Use Apache Arrow para evitar la necesidad de construir y mantener tuberías complejas de ETL
  • Analice datos a través de fuentes de datos dispares sin tener que consolidarlos en un repositorio centralizado.
Audiencia
  • Científicos de datos
  • Ingenieros de datos
Formato del curso
  • Conferencia parcial, discusión parcial, ejercicios y práctica práctica.
Nota
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => [outline] =>

Introduction

  • Apache Arrow vs Parquet

Installing and Configuring Apache Arrow

Overview of Apache Arrow Features and Architecture

Exploring Data with Pandas and Apache Arrow

Exploring Data with Spark and Apache Arrow

Exploring Data with R and Apache Arrow

Exploring Data with MapD and Apache Arrow

Other Data Analysis Integrations

  • PySpark, Parquet files on S3, and Oracle tables and Elasticsearch indices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037432 [source_title] => Apache Arrow for Data Analysis across Disparate Data Sources [source_language] => en [cert_code] => [weight] => -976 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => arrow ) ) [2] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => apache-arrow [1] => guayaquil ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /apache-arrow/cursos/guayaquil ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/apache-arrow/cursos/guayaquil Undefined array key "nobleprog_price_rounding" /apps/nobleprog-website/includes/functions/course-prices.php:289 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 289 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "nobleprog_price_rounding" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 289 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 5437 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => arrow [venue_id] => ec_15661446 [vfdc] => 175.00 [vadc] => 60.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => arrow ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [arrow] => stdClass Object ( [course_code] => arrow [hr_nid] => 284282 [title] => Apache Arrow for Data Analysis across Disparate Data Sources [requirements] =>
  • A basic undersanding of SQL
  • Familiarity with Python or R
  • Some familiarity with Apache Spark
[overview] => Apache Arrow es un marco de procesamiento de datos en memoria de código abierto. A menudo se usa junto con otras herramientas de ciencia de datos para acceder a almacenes de datos dispares para su análisis. Se integra bien con otras tecnologías, como bases de datos de GPU , bibliotecas y herramientas de aprendizaje automático, motores de ejecución y marcos de visualización de datos. En esta capacitación en vivo dirigida por un instructor en el sitio, los participantes aprenderán cómo integrar Apache Arrow con varios marcos de Data Science para acceder a datos de fuentes de datos dispares. Al final de esta capacitación, los participantes podrán:
  • Instale y configure Apache Arrow en un entorno agrupado distribuido
  • Use Apache Arrow para acceder a datos de fuentes de datos dispares
  • Use Apache Arrow para evitar la necesidad de construir y mantener tuberías complejas de ETL
  • Analice datos a través de fuentes de datos dispares sin tener que consolidarlos en un repositorio centralizado.
Audiencia
  • Científicos de datos
  • Ingenieros de datos
Formato del curso
  • Conferencia parcial, discusión parcial, ejercicios y práctica práctica.
Nota
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => [outline] =>

Introduction

  • Apache Arrow vs Parquet

Installing and Configuring Apache Arrow

Overview of Apache Arrow Features and Architecture

Exploring Data with Pandas and Apache Arrow

Exploring Data with Spark and Apache Arrow

Exploring Data with R and Apache Arrow

Exploring Data with MapD and Apache Arrow

Other Data Analysis Integrations

  • PySpark, Parquet files on S3, and Oracle tables and Elasticsearch indices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037432 [source_title] => Apache Arrow for Data Analysis across Disparate Data Sources [source_language] => en [cert_code] => [weight] => -976 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => arrow ) ) [1] => Array ( [0] => ec_4966 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => arrow [hr_nid] => 284282 [title] => Apache Arrow for Data Analysis across Disparate Data Sources [requirements] =>
  • A basic undersanding of SQL
  • Familiarity with Python or R
  • Some familiarity with Apache Spark
[overview] => Apache Arrow es un marco de procesamiento de datos en memoria de código abierto. A menudo se usa junto con otras herramientas de ciencia de datos para acceder a almacenes de datos dispares para su análisis. Se integra bien con otras tecnologías, como bases de datos de GPU , bibliotecas y herramientas de aprendizaje automático, motores de ejecución y marcos de visualización de datos. En esta capacitación en vivo dirigida por un instructor en el sitio, los participantes aprenderán cómo integrar Apache Arrow con varios marcos de Data Science para acceder a datos de fuentes de datos dispares. Al final de esta capacitación, los participantes podrán:
  • Instale y configure Apache Arrow en un entorno agrupado distribuido
  • Use Apache Arrow para acceder a datos de fuentes de datos dispares
  • Use Apache Arrow para evitar la necesidad de construir y mantener tuberías complejas de ETL
  • Analice datos a través de fuentes de datos dispares sin tener que consolidarlos en un repositorio centralizado.
Audiencia
  • Científicos de datos
  • Ingenieros de datos
Formato del curso
  • Conferencia parcial, discusión parcial, ejercicios y práctica práctica.
Nota
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => [outline] =>

Introduction

  • Apache Arrow vs Parquet

Installing and Configuring Apache Arrow

Overview of Apache Arrow Features and Architecture

Exploring Data with Pandas and Apache Arrow

Exploring Data with Spark and Apache Arrow

Exploring Data with R and Apache Arrow

Exploring Data with MapD and Apache Arrow

Other Data Analysis Integrations

  • PySpark, Parquet files on S3, and Oracle tables and Elasticsearch indices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037432 [source_title] => Apache Arrow for Data Analysis across Disparate Data Sources [source_language] => en [cert_code] => [weight] => -976 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => arrow ) ) [2] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => apache-arrow [1] => guayaquil ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /apache-arrow/cursos/guayaquil ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/apache-arrow/cursos/guayaquil Undefined array key "sdp" /apps/nobleprog-website/includes/functions/course-prices.php:281 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 281 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "sdp" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 281 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 5437 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => arrow [venue_id] => ec_15661447 [vfdc] => 200.00 [vadc] => 50.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => arrow ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [arrow] => stdClass Object ( [course_code] => arrow [hr_nid] => 284282 [title] => Apache Arrow for Data Analysis across Disparate Data Sources [requirements] =>
  • A basic undersanding of SQL
  • Familiarity with Python or R
  • Some familiarity with Apache Spark
[overview] => Apache Arrow es un marco de procesamiento de datos en memoria de código abierto. A menudo se usa junto con otras herramientas de ciencia de datos para acceder a almacenes de datos dispares para su análisis. Se integra bien con otras tecnologías, como bases de datos de GPU , bibliotecas y herramientas de aprendizaje automático, motores de ejecución y marcos de visualización de datos. En esta capacitación en vivo dirigida por un instructor en el sitio, los participantes aprenderán cómo integrar Apache Arrow con varios marcos de Data Science para acceder a datos de fuentes de datos dispares. Al final de esta capacitación, los participantes podrán:
  • Instale y configure Apache Arrow en un entorno agrupado distribuido
  • Use Apache Arrow para acceder a datos de fuentes de datos dispares
  • Use Apache Arrow para evitar la necesidad de construir y mantener tuberías complejas de ETL
  • Analice datos a través de fuentes de datos dispares sin tener que consolidarlos en un repositorio centralizado.
Audiencia
  • Científicos de datos
  • Ingenieros de datos
Formato del curso
  • Conferencia parcial, discusión parcial, ejercicios y práctica práctica.
Nota
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => [outline] =>

Introduction

  • Apache Arrow vs Parquet

Installing and Configuring Apache Arrow

Overview of Apache Arrow Features and Architecture

Exploring Data with Pandas and Apache Arrow

Exploring Data with Spark and Apache Arrow

Exploring Data with R and Apache Arrow

Exploring Data with MapD and Apache Arrow

Other Data Analysis Integrations

  • PySpark, Parquet files on S3, and Oracle tables and Elasticsearch indices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037432 [source_title] => Apache Arrow for Data Analysis across Disparate Data Sources [source_language] => en [cert_code] => [weight] => -976 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => arrow ) ) [1] => Array ( [0] => ec_4966 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => arrow [hr_nid] => 284282 [title] => Apache Arrow for Data Analysis across Disparate Data Sources [requirements] =>
  • A basic undersanding of SQL
  • Familiarity with Python or R
  • Some familiarity with Apache Spark
[overview] => Apache Arrow es un marco de procesamiento de datos en memoria de código abierto. A menudo se usa junto con otras herramientas de ciencia de datos para acceder a almacenes de datos dispares para su análisis. Se integra bien con otras tecnologías, como bases de datos de GPU , bibliotecas y herramientas de aprendizaje automático, motores de ejecución y marcos de visualización de datos. En esta capacitación en vivo dirigida por un instructor en el sitio, los participantes aprenderán cómo integrar Apache Arrow con varios marcos de Data Science para acceder a datos de fuentes de datos dispares. Al final de esta capacitación, los participantes podrán:
  • Instale y configure Apache Arrow en un entorno agrupado distribuido
  • Use Apache Arrow para acceder a datos de fuentes de datos dispares
  • Use Apache Arrow para evitar la necesidad de construir y mantener tuberías complejas de ETL
  • Analice datos a través de fuentes de datos dispares sin tener que consolidarlos en un repositorio centralizado.
Audiencia
  • Científicos de datos
  • Ingenieros de datos
Formato del curso
  • Conferencia parcial, discusión parcial, ejercicios y práctica práctica.
Nota
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => [outline] =>

Introduction

  • Apache Arrow vs Parquet

Installing and Configuring Apache Arrow

Overview of Apache Arrow Features and Architecture

Exploring Data with Pandas and Apache Arrow

Exploring Data with Spark and Apache Arrow

Exploring Data with R and Apache Arrow

Exploring Data with MapD and Apache Arrow

Other Data Analysis Integrations

  • PySpark, Parquet files on S3, and Oracle tables and Elasticsearch indices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037432 [source_title] => Apache Arrow for Data Analysis across Disparate Data Sources [source_language] => en [cert_code] => [weight] => -976 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => arrow ) ) [2] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => apache-arrow [1] => guayaquil ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /apache-arrow/cursos/guayaquil ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/apache-arrow/cursos/guayaquil Undefined array key "nobleprog_default_trainer_journey" /apps/nobleprog-website/includes/functions/course-prices.php:286 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 286 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "nobleprog_default_trainer_journey" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 286 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 5437 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => arrow [venue_id] => ec_15661447 [vfdc] => 200.00 [vadc] => 50.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => arrow ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [arrow] => stdClass Object ( [course_code] => arrow [hr_nid] => 284282 [title] => Apache Arrow for Data Analysis across Disparate Data Sources [requirements] =>
  • A basic undersanding of SQL
  • Familiarity with Python or R
  • Some familiarity with Apache Spark
[overview] => Apache Arrow es un marco de procesamiento de datos en memoria de código abierto. A menudo se usa junto con otras herramientas de ciencia de datos para acceder a almacenes de datos dispares para su análisis. Se integra bien con otras tecnologías, como bases de datos de GPU , bibliotecas y herramientas de aprendizaje automático, motores de ejecución y marcos de visualización de datos. En esta capacitación en vivo dirigida por un instructor en el sitio, los participantes aprenderán cómo integrar Apache Arrow con varios marcos de Data Science para acceder a datos de fuentes de datos dispares. Al final de esta capacitación, los participantes podrán:
  • Instale y configure Apache Arrow en un entorno agrupado distribuido
  • Use Apache Arrow para acceder a datos de fuentes de datos dispares
  • Use Apache Arrow para evitar la necesidad de construir y mantener tuberías complejas de ETL
  • Analice datos a través de fuentes de datos dispares sin tener que consolidarlos en un repositorio centralizado.
Audiencia
  • Científicos de datos
  • Ingenieros de datos
Formato del curso
  • Conferencia parcial, discusión parcial, ejercicios y práctica práctica.
Nota
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => [outline] =>

Introduction

  • Apache Arrow vs Parquet

Installing and Configuring Apache Arrow

Overview of Apache Arrow Features and Architecture

Exploring Data with Pandas and Apache Arrow

Exploring Data with Spark and Apache Arrow

Exploring Data with R and Apache Arrow

Exploring Data with MapD and Apache Arrow

Other Data Analysis Integrations

  • PySpark, Parquet files on S3, and Oracle tables and Elasticsearch indices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037432 [source_title] => Apache Arrow for Data Analysis across Disparate Data Sources [source_language] => en [cert_code] => [weight] => -976 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => arrow ) ) [1] => Array ( [0] => ec_4966 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => arrow [hr_nid] => 284282 [title] => Apache Arrow for Data Analysis across Disparate Data Sources [requirements] =>
  • A basic undersanding of SQL
  • Familiarity with Python or R
  • Some familiarity with Apache Spark
[overview] => Apache Arrow es un marco de procesamiento de datos en memoria de código abierto. A menudo se usa junto con otras herramientas de ciencia de datos para acceder a almacenes de datos dispares para su análisis. Se integra bien con otras tecnologías, como bases de datos de GPU , bibliotecas y herramientas de aprendizaje automático, motores de ejecución y marcos de visualización de datos. En esta capacitación en vivo dirigida por un instructor en el sitio, los participantes aprenderán cómo integrar Apache Arrow con varios marcos de Data Science para acceder a datos de fuentes de datos dispares. Al final de esta capacitación, los participantes podrán:
  • Instale y configure Apache Arrow en un entorno agrupado distribuido
  • Use Apache Arrow para acceder a datos de fuentes de datos dispares
  • Use Apache Arrow para evitar la necesidad de construir y mantener tuberías complejas de ETL
  • Analice datos a través de fuentes de datos dispares sin tener que consolidarlos en un repositorio centralizado.
Audiencia
  • Científicos de datos
  • Ingenieros de datos
Formato del curso
  • Conferencia parcial, discusión parcial, ejercicios y práctica práctica.
Nota
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => [outline] =>

Introduction

  • Apache Arrow vs Parquet

Installing and Configuring Apache Arrow

Overview of Apache Arrow Features and Architecture

Exploring Data with Pandas and Apache Arrow

Exploring Data with Spark and Apache Arrow

Exploring Data with R and Apache Arrow

Exploring Data with MapD and Apache Arrow

Other Data Analysis Integrations

  • PySpark, Parquet files on S3, and Oracle tables and Elasticsearch indices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037432 [source_title] => Apache Arrow for Data Analysis across Disparate Data Sources [source_language] => en [cert_code] => [weight] => -976 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => arrow ) ) [2] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => apache-arrow [1] => guayaquil ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /apache-arrow/cursos/guayaquil ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/apache-arrow/cursos/guayaquil Undefined array key "nobleprog_price_rounding" /apps/nobleprog-website/includes/functions/course-prices.php:289 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 289 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "nobleprog_price_rounding" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 289 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 5437 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => arrow [venue_id] => ec_15661447 [vfdc] => 200.00 [vadc] => 50.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => arrow ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [arrow] => stdClass Object ( [course_code] => arrow [hr_nid] => 284282 [title] => Apache Arrow for Data Analysis across Disparate Data Sources [requirements] =>
  • A basic undersanding of SQL
  • Familiarity with Python or R
  • Some familiarity with Apache Spark
[overview] => Apache Arrow es un marco de procesamiento de datos en memoria de código abierto. A menudo se usa junto con otras herramientas de ciencia de datos para acceder a almacenes de datos dispares para su análisis. Se integra bien con otras tecnologías, como bases de datos de GPU , bibliotecas y herramientas de aprendizaje automático, motores de ejecución y marcos de visualización de datos. En esta capacitación en vivo dirigida por un instructor en el sitio, los participantes aprenderán cómo integrar Apache Arrow con varios marcos de Data Science para acceder a datos de fuentes de datos dispares. Al final de esta capacitación, los participantes podrán:
  • Instale y configure Apache Arrow en un entorno agrupado distribuido
  • Use Apache Arrow para acceder a datos de fuentes de datos dispares
  • Use Apache Arrow para evitar la necesidad de construir y mantener tuberías complejas de ETL
  • Analice datos a través de fuentes de datos dispares sin tener que consolidarlos en un repositorio centralizado.
Audiencia
  • Científicos de datos
  • Ingenieros de datos
Formato del curso
  • Conferencia parcial, discusión parcial, ejercicios y práctica práctica.
Nota
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => [outline] =>

Introduction

  • Apache Arrow vs Parquet

Installing and Configuring Apache Arrow

Overview of Apache Arrow Features and Architecture

Exploring Data with Pandas and Apache Arrow

Exploring Data with Spark and Apache Arrow

Exploring Data with R and Apache Arrow

Exploring Data with MapD and Apache Arrow

Other Data Analysis Integrations

  • PySpark, Parquet files on S3, and Oracle tables and Elasticsearch indices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037432 [source_title] => Apache Arrow for Data Analysis across Disparate Data Sources [source_language] => en [cert_code] => [weight] => -976 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => arrow ) ) [1] => Array ( [0] => ec_4966 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => arrow [hr_nid] => 284282 [title] => Apache Arrow for Data Analysis across Disparate Data Sources [requirements] =>
  • A basic undersanding of SQL
  • Familiarity with Python or R
  • Some familiarity with Apache Spark
[overview] => Apache Arrow es un marco de procesamiento de datos en memoria de código abierto. A menudo se usa junto con otras herramientas de ciencia de datos para acceder a almacenes de datos dispares para su análisis. Se integra bien con otras tecnologías, como bases de datos de GPU , bibliotecas y herramientas de aprendizaje automático, motores de ejecución y marcos de visualización de datos. En esta capacitación en vivo dirigida por un instructor en el sitio, los participantes aprenderán cómo integrar Apache Arrow con varios marcos de Data Science para acceder a datos de fuentes de datos dispares. Al final de esta capacitación, los participantes podrán:
  • Instale y configure Apache Arrow en un entorno agrupado distribuido
  • Use Apache Arrow para acceder a datos de fuentes de datos dispares
  • Use Apache Arrow para evitar la necesidad de construir y mantener tuberías complejas de ETL
  • Analice datos a través de fuentes de datos dispares sin tener que consolidarlos en un repositorio centralizado.
Audiencia
  • Científicos de datos
  • Ingenieros de datos
Formato del curso
  • Conferencia parcial, discusión parcial, ejercicios y práctica práctica.
Nota
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => [outline] =>

Introduction

  • Apache Arrow vs Parquet

Installing and Configuring Apache Arrow

Overview of Apache Arrow Features and Architecture

Exploring Data with Pandas and Apache Arrow

Exploring Data with Spark and Apache Arrow

Exploring Data with R and Apache Arrow

Exploring Data with MapD and Apache Arrow

Other Data Analysis Integrations

  • PySpark, Parquet files on S3, and Oracle tables and Elasticsearch indices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037432 [source_title] => Apache Arrow for Data Analysis across Disparate Data Sources [source_language] => en [cert_code] => [weight] => -976 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => arrow ) ) [2] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => apache-arrow [1] => guayaquil ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /apache-arrow/cursos/guayaquil ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/apache-arrow/cursos/guayaquil Undefined array key "sdp" /apps/nobleprog-website/includes/functions/course-prices.php:281 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 281 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "sdp" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 281 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 5437 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => arrow [venue_id] => ec_15661446 [vfdc] => 175.00 [vadc] => 60.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => arrow ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [arrow] => stdClass Object ( [course_code] => arrow [hr_nid] => 284282 [title] => Apache Arrow for Data Analysis across Disparate Data Sources [requirements] =>
  • A basic undersanding of SQL
  • Familiarity with Python or R
  • Some familiarity with Apache Spark
[overview] => Apache Arrow es un marco de procesamiento de datos en memoria de código abierto. A menudo se usa junto con otras herramientas de ciencia de datos para acceder a almacenes de datos dispares para su análisis. Se integra bien con otras tecnologías, como bases de datos de GPU , bibliotecas y herramientas de aprendizaje automático, motores de ejecución y marcos de visualización de datos. En esta capacitación en vivo dirigida por un instructor en el sitio, los participantes aprenderán cómo integrar Apache Arrow con varios marcos de Data Science para acceder a datos de fuentes de datos dispares. Al final de esta capacitación, los participantes podrán:
  • Instale y configure Apache Arrow en un entorno agrupado distribuido
  • Use Apache Arrow para acceder a datos de fuentes de datos dispares
  • Use Apache Arrow para evitar la necesidad de construir y mantener tuberías complejas de ETL
  • Analice datos a través de fuentes de datos dispares sin tener que consolidarlos en un repositorio centralizado.
Audiencia
  • Científicos de datos
  • Ingenieros de datos
Formato del curso
  • Conferencia parcial, discusión parcial, ejercicios y práctica práctica.
Nota
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => [outline] =>

Introduction

  • Apache Arrow vs Parquet

Installing and Configuring Apache Arrow

Overview of Apache Arrow Features and Architecture

Exploring Data with Pandas and Apache Arrow

Exploring Data with Spark and Apache Arrow

Exploring Data with R and Apache Arrow

Exploring Data with MapD and Apache Arrow

Other Data Analysis Integrations

  • PySpark, Parquet files on S3, and Oracle tables and Elasticsearch indices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037432 [source_title] => Apache Arrow for Data Analysis across Disparate Data Sources [source_language] => en [cert_code] => [weight] => -976 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => arrow ) ) [1] => Array ( [0] => ec_4966 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => arrow [hr_nid] => 284282 [title] => Apache Arrow for Data Analysis across Disparate Data Sources [requirements] =>
  • A basic undersanding of SQL
  • Familiarity with Python or R
  • Some familiarity with Apache Spark
[overview] => Apache Arrow es un marco de procesamiento de datos en memoria de código abierto. A menudo se usa junto con otras herramientas de ciencia de datos para acceder a almacenes de datos dispares para su análisis. Se integra bien con otras tecnologías, como bases de datos de GPU , bibliotecas y herramientas de aprendizaje automático, motores de ejecución y marcos de visualización de datos. En esta capacitación en vivo dirigida por un instructor en el sitio, los participantes aprenderán cómo integrar Apache Arrow con varios marcos de Data Science para acceder a datos de fuentes de datos dispares. Al final de esta capacitación, los participantes podrán:
  • Instale y configure Apache Arrow en un entorno agrupado distribuido
  • Use Apache Arrow para acceder a datos de fuentes de datos dispares
  • Use Apache Arrow para evitar la necesidad de construir y mantener tuberías complejas de ETL
  • Analice datos a través de fuentes de datos dispares sin tener que consolidarlos en un repositorio centralizado.
Audiencia
  • Científicos de datos
  • Ingenieros de datos
Formato del curso
  • Conferencia parcial, discusión parcial, ejercicios y práctica práctica.
Nota
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => [outline] =>

Introduction

  • Apache Arrow vs Parquet

Installing and Configuring Apache Arrow

Overview of Apache Arrow Features and Architecture

Exploring Data with Pandas and Apache Arrow

Exploring Data with Spark and Apache Arrow

Exploring Data with R and Apache Arrow

Exploring Data with MapD and Apache Arrow

Other Data Analysis Integrations

  • PySpark, Parquet files on S3, and Oracle tables and Elasticsearch indices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037432 [source_title] => Apache Arrow for Data Analysis across Disparate Data Sources [source_language] => en [cert_code] => [weight] => -976 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => arrow ) ) [2] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => apache-arrow [1] => guayaquil ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /apache-arrow/cursos/guayaquil ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/apache-arrow/cursos/guayaquil Undefined array key "nobleprog_default_trainer_journey" /apps/nobleprog-website/includes/functions/course-prices.php:286 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 286 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "nobleprog_default_trainer_journey" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 286 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 5437 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => arrow [venue_id] => ec_15661446 [vfdc] => 175.00 [vadc] => 60.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => arrow ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [arrow] => stdClass Object ( [course_code] => arrow [hr_nid] => 284282 [title] => Apache Arrow for Data Analysis across Disparate Data Sources [requirements] =>
  • A basic undersanding of SQL
  • Familiarity with Python or R
  • Some familiarity with Apache Spark
[overview] => Apache Arrow es un marco de procesamiento de datos en memoria de código abierto. A menudo se usa junto con otras herramientas de ciencia de datos para acceder a almacenes de datos dispares para su análisis. Se integra bien con otras tecnologías, como bases de datos de GPU , bibliotecas y herramientas de aprendizaje automático, motores de ejecución y marcos de visualización de datos. En esta capacitación en vivo dirigida por un instructor en el sitio, los participantes aprenderán cómo integrar Apache Arrow con varios marcos de Data Science para acceder a datos de fuentes de datos dispares. Al final de esta capacitación, los participantes podrán:
  • Instale y configure Apache Arrow en un entorno agrupado distribuido
  • Use Apache Arrow para acceder a datos de fuentes de datos dispares
  • Use Apache Arrow para evitar la necesidad de construir y mantener tuberías complejas de ETL
  • Analice datos a través de fuentes de datos dispares sin tener que consolidarlos en un repositorio centralizado.
Audiencia
  • Científicos de datos
  • Ingenieros de datos
Formato del curso
  • Conferencia parcial, discusión parcial, ejercicios y práctica práctica.
Nota
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => [outline] =>

Introduction

  • Apache Arrow vs Parquet

Installing and Configuring Apache Arrow

Overview of Apache Arrow Features and Architecture

Exploring Data with Pandas and Apache Arrow

Exploring Data with Spark and Apache Arrow

Exploring Data with R and Apache Arrow

Exploring Data with MapD and Apache Arrow

Other Data Analysis Integrations

  • PySpark, Parquet files on S3, and Oracle tables and Elasticsearch indices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037432 [source_title] => Apache Arrow for Data Analysis across Disparate Data Sources [source_language] => en [cert_code] => [weight] => -976 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => arrow ) ) [1] => Array ( [0] => ec_4966 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => arrow [hr_nid] => 284282 [title] => Apache Arrow for Data Analysis across Disparate Data Sources [requirements] =>
  • A basic undersanding of SQL
  • Familiarity with Python or R
  • Some familiarity with Apache Spark
[overview] => Apache Arrow es un marco de procesamiento de datos en memoria de código abierto. A menudo se usa junto con otras herramientas de ciencia de datos para acceder a almacenes de datos dispares para su análisis. Se integra bien con otras tecnologías, como bases de datos de GPU , bibliotecas y herramientas de aprendizaje automático, motores de ejecución y marcos de visualización de datos. En esta capacitación en vivo dirigida por un instructor en el sitio, los participantes aprenderán cómo integrar Apache Arrow con varios marcos de Data Science para acceder a datos de fuentes de datos dispares. Al final de esta capacitación, los participantes podrán:
  • Instale y configure Apache Arrow en un entorno agrupado distribuido
  • Use Apache Arrow para acceder a datos de fuentes de datos dispares
  • Use Apache Arrow para evitar la necesidad de construir y mantener tuberías complejas de ETL
  • Analice datos a través de fuentes de datos dispares sin tener que consolidarlos en un repositorio centralizado.
Audiencia
  • Científicos de datos
  • Ingenieros de datos
Formato del curso
  • Conferencia parcial, discusión parcial, ejercicios y práctica práctica.
Nota
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => [outline] =>

Introduction

  • Apache Arrow vs Parquet

Installing and Configuring Apache Arrow

Overview of Apache Arrow Features and Architecture

Exploring Data with Pandas and Apache Arrow

Exploring Data with Spark and Apache Arrow

Exploring Data with R and Apache Arrow

Exploring Data with MapD and Apache Arrow

Other Data Analysis Integrations

  • PySpark, Parquet files on S3, and Oracle tables and Elasticsearch indices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037432 [source_title] => Apache Arrow for Data Analysis across Disparate Data Sources [source_language] => en [cert_code] => [weight] => -976 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => arrow ) ) [2] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => apache-arrow [1] => guayaquil ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /apache-arrow/cursos/guayaquil ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/apache-arrow/cursos/guayaquil Undefined array key "nobleprog_price_rounding" /apps/nobleprog-website/includes/functions/course-prices.php:289 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 289 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "nobleprog_price_rounding" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 289 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 5437 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => arrow [venue_id] => ec_15661446 [vfdc] => 175.00 [vadc] => 60.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => arrow ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [arrow] => stdClass Object ( [course_code] => arrow [hr_nid] => 284282 [title] => Apache Arrow for Data Analysis across Disparate Data Sources [requirements] =>
  • A basic undersanding of SQL
  • Familiarity with Python or R
  • Some familiarity with Apache Spark
[overview] => Apache Arrow es un marco de procesamiento de datos en memoria de código abierto. A menudo se usa junto con otras herramientas de ciencia de datos para acceder a almacenes de datos dispares para su análisis. Se integra bien con otras tecnologías, como bases de datos de GPU , bibliotecas y herramientas de aprendizaje automático, motores de ejecución y marcos de visualización de datos. En esta capacitación en vivo dirigida por un instructor en el sitio, los participantes aprenderán cómo integrar Apache Arrow con varios marcos de Data Science para acceder a datos de fuentes de datos dispares. Al final de esta capacitación, los participantes podrán:
  • Instale y configure Apache Arrow en un entorno agrupado distribuido
  • Use Apache Arrow para acceder a datos de fuentes de datos dispares
  • Use Apache Arrow para evitar la necesidad de construir y mantener tuberías complejas de ETL
  • Analice datos a través de fuentes de datos dispares sin tener que consolidarlos en un repositorio centralizado.
Audiencia
  • Científicos de datos
  • Ingenieros de datos
Formato del curso
  • Conferencia parcial, discusión parcial, ejercicios y práctica práctica.
Nota
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => [outline] =>

Introduction

  • Apache Arrow vs Parquet

Installing and Configuring Apache Arrow

Overview of Apache Arrow Features and Architecture

Exploring Data with Pandas and Apache Arrow

Exploring Data with Spark and Apache Arrow

Exploring Data with R and Apache Arrow

Exploring Data with MapD and Apache Arrow

Other Data Analysis Integrations

  • PySpark, Parquet files on S3, and Oracle tables and Elasticsearch indices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037432 [source_title] => Apache Arrow for Data Analysis across Disparate Data Sources [source_language] => en [cert_code] => [weight] => -976 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => arrow ) ) [1] => Array ( [0] => ec_4966 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => arrow [hr_nid] => 284282 [title] => Apache Arrow for Data Analysis across Disparate Data Sources [requirements] =>
  • A basic undersanding of SQL
  • Familiarity with Python or R
  • Some familiarity with Apache Spark
[overview] => Apache Arrow es un marco de procesamiento de datos en memoria de código abierto. A menudo se usa junto con otras herramientas de ciencia de datos para acceder a almacenes de datos dispares para su análisis. Se integra bien con otras tecnologías, como bases de datos de GPU , bibliotecas y herramientas de aprendizaje automático, motores de ejecución y marcos de visualización de datos. En esta capacitación en vivo dirigida por un instructor en el sitio, los participantes aprenderán cómo integrar Apache Arrow con varios marcos de Data Science para acceder a datos de fuentes de datos dispares. Al final de esta capacitación, los participantes podrán:
  • Instale y configure Apache Arrow en un entorno agrupado distribuido
  • Use Apache Arrow para acceder a datos de fuentes de datos dispares
  • Use Apache Arrow para evitar la necesidad de construir y mantener tuberías complejas de ETL
  • Analice datos a través de fuentes de datos dispares sin tener que consolidarlos en un repositorio centralizado.
Audiencia
  • Científicos de datos
  • Ingenieros de datos
Formato del curso
  • Conferencia parcial, discusión parcial, ejercicios y práctica práctica.
Nota
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => [outline] =>

Introduction

  • Apache Arrow vs Parquet

Installing and Configuring Apache Arrow

Overview of Apache Arrow Features and Architecture

Exploring Data with Pandas and Apache Arrow

Exploring Data with Spark and Apache Arrow

Exploring Data with R and Apache Arrow

Exploring Data with MapD and Apache Arrow

Other Data Analysis Integrations

  • PySpark, Parquet files on S3, and Oracle tables and Elasticsearch indices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037432 [source_title] => Apache Arrow for Data Analysis across Disparate Data Sources [source_language] => en [cert_code] => [weight] => -976 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => arrow ) ) [2] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => apache-arrow [1] => guayaquil ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /apache-arrow/cursos/guayaquil ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/apache-arrow/cursos/guayaquil Undefined array key "sdp" /apps/nobleprog-website/includes/functions/course-prices.php:281 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 281 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "sdp" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 281 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 5437 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => arrow [venue_id] => ec_15661447 [vfdc] => 200.00 [vadc] => 50.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => arrow ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [arrow] => stdClass Object ( [course_code] => arrow [hr_nid] => 284282 [title] => Apache Arrow for Data Analysis across Disparate Data Sources [requirements] =>
  • A basic undersanding of SQL
  • Familiarity with Python or R
  • Some familiarity with Apache Spark
[overview] => Apache Arrow es un marco de procesamiento de datos en memoria de código abierto. A menudo se usa junto con otras herramientas de ciencia de datos para acceder a almacenes de datos dispares para su análisis. Se integra bien con otras tecnologías, como bases de datos de GPU , bibliotecas y herramientas de aprendizaje automático, motores de ejecución y marcos de visualización de datos. En esta capacitación en vivo dirigida por un instructor en el sitio, los participantes aprenderán cómo integrar Apache Arrow con varios marcos de Data Science para acceder a datos de fuentes de datos dispares. Al final de esta capacitación, los participantes podrán:
  • Instale y configure Apache Arrow en un entorno agrupado distribuido
  • Use Apache Arrow para acceder a datos de fuentes de datos dispares
  • Use Apache Arrow para evitar la necesidad de construir y mantener tuberías complejas de ETL
  • Analice datos a través de fuentes de datos dispares sin tener que consolidarlos en un repositorio centralizado.
Audiencia
  • Científicos de datos
  • Ingenieros de datos
Formato del curso
  • Conferencia parcial, discusión parcial, ejercicios y práctica práctica.
Nota
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => [outline] =>

Introduction

  • Apache Arrow vs Parquet

Installing and Configuring Apache Arrow

Overview of Apache Arrow Features and Architecture

Exploring Data with Pandas and Apache Arrow

Exploring Data with Spark and Apache Arrow

Exploring Data with R and Apache Arrow

Exploring Data with MapD and Apache Arrow

Other Data Analysis Integrations

  • PySpark, Parquet files on S3, and Oracle tables and Elasticsearch indices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037432 [source_title] => Apache Arrow for Data Analysis across Disparate Data Sources [source_language] => en [cert_code] => [weight] => -976 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => arrow ) ) [1] => Array ( [0] => ec_4966 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => arrow [hr_nid] => 284282 [title] => Apache Arrow for Data Analysis across Disparate Data Sources [requirements] =>
  • A basic undersanding of SQL
  • Familiarity with Python or R
  • Some familiarity with Apache Spark
[overview] => Apache Arrow es un marco de procesamiento de datos en memoria de código abierto. A menudo se usa junto con otras herramientas de ciencia de datos para acceder a almacenes de datos dispares para su análisis. Se integra bien con otras tecnologías, como bases de datos de GPU , bibliotecas y herramientas de aprendizaje automático, motores de ejecución y marcos de visualización de datos. En esta capacitación en vivo dirigida por un instructor en el sitio, los participantes aprenderán cómo integrar Apache Arrow con varios marcos de Data Science para acceder a datos de fuentes de datos dispares. Al final de esta capacitación, los participantes podrán:
  • Instale y configure Apache Arrow en un entorno agrupado distribuido
  • Use Apache Arrow para acceder a datos de fuentes de datos dispares
  • Use Apache Arrow para evitar la necesidad de construir y mantener tuberías complejas de ETL
  • Analice datos a través de fuentes de datos dispares sin tener que consolidarlos en un repositorio centralizado.
Audiencia
  • Científicos de datos
  • Ingenieros de datos
Formato del curso
  • Conferencia parcial, discusión parcial, ejercicios y práctica práctica.
Nota
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => [outline] =>

Introduction

  • Apache Arrow vs Parquet

Installing and Configuring Apache Arrow

Overview of Apache Arrow Features and Architecture

Exploring Data with Pandas and Apache Arrow

Exploring Data with Spark and Apache Arrow

Exploring Data with R and Apache Arrow

Exploring Data with MapD and Apache Arrow

Other Data Analysis Integrations

  • PySpark, Parquet files on S3, and Oracle tables and Elasticsearch indices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037432 [source_title] => Apache Arrow for Data Analysis across Disparate Data Sources [source_language] => en [cert_code] => [weight] => -976 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => arrow ) ) [2] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => apache-arrow [1] => guayaquil ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /apache-arrow/cursos/guayaquil ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/apache-arrow/cursos/guayaquil Undefined array key "nobleprog_default_trainer_journey" /apps/nobleprog-website/includes/functions/course-prices.php:286 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 286 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "nobleprog_default_trainer_journey" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 286 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 5437 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => arrow [venue_id] => ec_15661447 [vfdc] => 200.00 [vadc] => 50.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => arrow ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [arrow] => stdClass Object ( [course_code] => arrow [hr_nid] => 284282 [title] => Apache Arrow for Data Analysis across Disparate Data Sources [requirements] =>
  • A basic undersanding of SQL
  • Familiarity with Python or R
  • Some familiarity with Apache Spark
[overview] => Apache Arrow es un marco de procesamiento de datos en memoria de código abierto. A menudo se usa junto con otras herramientas de ciencia de datos para acceder a almacenes de datos dispares para su análisis. Se integra bien con otras tecnologías, como bases de datos de GPU , bibliotecas y herramientas de aprendizaje automático, motores de ejecución y marcos de visualización de datos. En esta capacitación en vivo dirigida por un instructor en el sitio, los participantes aprenderán cómo integrar Apache Arrow con varios marcos de Data Science para acceder a datos de fuentes de datos dispares. Al final de esta capacitación, los participantes podrán:
  • Instale y configure Apache Arrow en un entorno agrupado distribuido
  • Use Apache Arrow para acceder a datos de fuentes de datos dispares
  • Use Apache Arrow para evitar la necesidad de construir y mantener tuberías complejas de ETL
  • Analice datos a través de fuentes de datos dispares sin tener que consolidarlos en un repositorio centralizado.
Audiencia
  • Científicos de datos
  • Ingenieros de datos
Formato del curso
  • Conferencia parcial, discusión parcial, ejercicios y práctica práctica.
Nota
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => [outline] =>

Introduction

  • Apache Arrow vs Parquet

Installing and Configuring Apache Arrow

Overview of Apache Arrow Features and Architecture

Exploring Data with Pandas and Apache Arrow

Exploring Data with Spark and Apache Arrow

Exploring Data with R and Apache Arrow

Exploring Data with MapD and Apache Arrow

Other Data Analysis Integrations

  • PySpark, Parquet files on S3, and Oracle tables and Elasticsearch indices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037432 [source_title] => Apache Arrow for Data Analysis across Disparate Data Sources [source_language] => en [cert_code] => [weight] => -976 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => arrow ) ) [1] => Array ( [0] => ec_4966 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => arrow [hr_nid] => 284282 [title] => Apache Arrow for Data Analysis across Disparate Data Sources [requirements] =>
  • A basic undersanding of SQL
  • Familiarity with Python or R
  • Some familiarity with Apache Spark
[overview] => Apache Arrow es un marco de procesamiento de datos en memoria de código abierto. A menudo se usa junto con otras herramientas de ciencia de datos para acceder a almacenes de datos dispares para su análisis. Se integra bien con otras tecnologías, como bases de datos de GPU , bibliotecas y herramientas de aprendizaje automático, motores de ejecución y marcos de visualización de datos. En esta capacitación en vivo dirigida por un instructor en el sitio, los participantes aprenderán cómo integrar Apache Arrow con varios marcos de Data Science para acceder a datos de fuentes de datos dispares. Al final de esta capacitación, los participantes podrán:
  • Instale y configure Apache Arrow en un entorno agrupado distribuido
  • Use Apache Arrow para acceder a datos de fuentes de datos dispares
  • Use Apache Arrow para evitar la necesidad de construir y mantener tuberías complejas de ETL
  • Analice datos a través de fuentes de datos dispares sin tener que consolidarlos en un repositorio centralizado.
Audiencia
  • Científicos de datos
  • Ingenieros de datos
Formato del curso
  • Conferencia parcial, discusión parcial, ejercicios y práctica práctica.
Nota
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => [outline] =>

Introduction

  • Apache Arrow vs Parquet

Installing and Configuring Apache Arrow

Overview of Apache Arrow Features and Architecture

Exploring Data with Pandas and Apache Arrow

Exploring Data with Spark and Apache Arrow

Exploring Data with R and Apache Arrow

Exploring Data with MapD and Apache Arrow

Other Data Analysis Integrations

  • PySpark, Parquet files on S3, and Oracle tables and Elasticsearch indices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037432 [source_title] => Apache Arrow for Data Analysis across Disparate Data Sources [source_language] => en [cert_code] => [weight] => -976 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => arrow ) ) [2] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => apache-arrow [1] => guayaquil ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /apache-arrow/cursos/guayaquil ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/apache-arrow/cursos/guayaquil Undefined array key "nobleprog_price_rounding" /apps/nobleprog-website/includes/functions/course-prices.php:289 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 289 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "nobleprog_price_rounding" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 289 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 5437 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => arrow [venue_id] => ec_15661447 [vfdc] => 200.00 [vadc] => 50.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-schedule.php [line] => 60 [function] => course_price_virtual_event_price [args] => Array ( [0] => arrow ) ) [3] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 368 [function] => np_upcoming_courses_schedule [args] => Array ( [0] => Array ( [arrow] => stdClass Object ( [course_code] => arrow [hr_nid] => 284282 [title] => Apache Arrow for Data Analysis across Disparate Data Sources [requirements] =>
  • A basic undersanding of SQL
  • Familiarity with Python or R
  • Some familiarity with Apache Spark
[overview] => Apache Arrow es un marco de procesamiento de datos en memoria de código abierto. A menudo se usa junto con otras herramientas de ciencia de datos para acceder a almacenes de datos dispares para su análisis. Se integra bien con otras tecnologías, como bases de datos de GPU , bibliotecas y herramientas de aprendizaje automático, motores de ejecución y marcos de visualización de datos. En esta capacitación en vivo dirigida por un instructor en el sitio, los participantes aprenderán cómo integrar Apache Arrow con varios marcos de Data Science para acceder a datos de fuentes de datos dispares. Al final de esta capacitación, los participantes podrán:
  • Instale y configure Apache Arrow en un entorno agrupado distribuido
  • Use Apache Arrow para acceder a datos de fuentes de datos dispares
  • Use Apache Arrow para evitar la necesidad de construir y mantener tuberías complejas de ETL
  • Analice datos a través de fuentes de datos dispares sin tener que consolidarlos en un repositorio centralizado.
Audiencia
  • Científicos de datos
  • Ingenieros de datos
Formato del curso
  • Conferencia parcial, discusión parcial, ejercicios y práctica práctica.
Nota
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => [outline] =>

Introduction

  • Apache Arrow vs Parquet

Installing and Configuring Apache Arrow

Overview of Apache Arrow Features and Architecture

Exploring Data with Pandas and Apache Arrow

Exploring Data with Spark and Apache Arrow

Exploring Data with R and Apache Arrow

Exploring Data with MapD and Apache Arrow

Other Data Analysis Integrations

  • PySpark, Parquet files on S3, and Oracle tables and Elasticsearch indices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037432 [source_title] => Apache Arrow for Data Analysis across Disparate Data Sources [source_language] => en [cert_code] => [weight] => -976 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => arrow ) ) [1] => Array ( [0] => ec_4966 ) ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 97 [function] => category_get_upcoming_courses [args] => Array ( [0] => Array ( [0] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) [1] => Array ( [region_id] => ec_4967 [region_name] => Quito [url_path_mapper] => quito [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) [1] => Array ( [0] => stdClass Object ( [course_code] => arrow [hr_nid] => 284282 [title] => Apache Arrow for Data Analysis across Disparate Data Sources [requirements] =>
  • A basic undersanding of SQL
  • Familiarity with Python or R
  • Some familiarity with Apache Spark
[overview] => Apache Arrow es un marco de procesamiento de datos en memoria de código abierto. A menudo se usa junto con otras herramientas de ciencia de datos para acceder a almacenes de datos dispares para su análisis. Se integra bien con otras tecnologías, como bases de datos de GPU , bibliotecas y herramientas de aprendizaje automático, motores de ejecución y marcos de visualización de datos. En esta capacitación en vivo dirigida por un instructor en el sitio, los participantes aprenderán cómo integrar Apache Arrow con varios marcos de Data Science para acceder a datos de fuentes de datos dispares. Al final de esta capacitación, los participantes podrán:
  • Instale y configure Apache Arrow en un entorno agrupado distribuido
  • Use Apache Arrow para acceder a datos de fuentes de datos dispares
  • Use Apache Arrow para evitar la necesidad de construir y mantener tuberías complejas de ETL
  • Analice datos a través de fuentes de datos dispares sin tener que consolidarlos en un repositorio centralizado.
Audiencia
  • Científicos de datos
  • Ingenieros de datos
Formato del curso
  • Conferencia parcial, discusión parcial, ejercicios y práctica práctica.
Nota
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
[category_overview] => [outline] =>

Introduction

  • Apache Arrow vs Parquet

Installing and Configuring Apache Arrow

Overview of Apache Arrow Features and Architecture

Exploring Data with Pandas and Apache Arrow

Exploring Data with Spark and Apache Arrow

Exploring Data with R and Apache Arrow

Exploring Data with MapD and Apache Arrow

Other Data Analysis Integrations

  • PySpark, Parquet files on S3, and Oracle tables and Elasticsearch indices

Troubleshooting

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037432 [source_title] => Apache Arrow for Data Analysis across Disparate Data Sources [source_language] => en [cert_code] => [weight] => -976 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => arrow ) ) [2] => Array ( [region_id] => ec_4966 [region_name] => Guayaquil [url_path_mapper] => guayaquil [locative_case_name] => [language] => es [region_publish_status] => 1 [sales_area] => ec_ecuador [parent_region] => ) ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => apache-arrow [1] => guayaquil ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /apache-arrow/cursos/guayaquil ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/apache-arrow/cursos/guayaquil Undefined property: stdClass::$machine_description /apps/hitra7/drupal7/sites/all/modules/_custom/frontend/nobleprog_frontend/nobleprog_frontend_category_description.logic.php:50 Array ( [0] => Array ( [file] => /apps/hitra7/drupal7/sites/all/modules/_custom/frontend/nobleprog_frontend/nobleprog_frontend_category_description.logic.php [line] => 50 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined property: stdClass::$machine_description [2] => /apps/hitra7/drupal7/sites/all/modules/_custom/frontend/nobleprog_frontend/nobleprog_frontend_category_description.logic.php [3] => 50 ) ) [1] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 107 [function] => nobleprog_frontend_get_category_description [args] => Array ( [0] => stdClass Object ( [tid] => 2983 [url_alias] => cursos-apache-arrow [name] => Apache Arrow [description] =>

En línea o en el sitio, los cursos de capacitación en vivo de Apache Arrow dirigidos por un instructor demuestran a través de la práctica interactiva cómo usar Apache Arrow para procesar datos de fuentes de datos dispares. La capacitación de Apache Arrow está disponible como "capacitación en vivo en línea" o "capacitación en vivo en el sitio". La capacitación en vivo en línea (también conocida como "capacitación remota en vivo") se lleva a cabo a través de un escritorio remoto interactivo La capacitación en vivo in situ se puede llevar a cabo localmente en las instalaciones del cliente en o en los centros de capacitación corporativa de NobleProg en . NobleProg -- Su proveedor local de capacitación

[language] => [url_path_mapper] => apache-arrow [english_name] => Apache Arrow [original_description] => Online or onsite, instructor-led live Apache Arrow training courses demonstrate through interactive hands-on practice how to use Apache Arrow to process data from disparate data sources.. Apache Arrow training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Onsite live training can be carried out locally on customer premises in or in NobleProg corporate training centers in . NobleProg -- Your Local Training Provider [consulting_options] => available ) [1] => Apache Arrow [2] => Guayaquil [3] => stdClass Object ( [language] => es [name] => Spanish [native] => Español [prefix] => es [default_language] => es [language_url] => [secondary_language] => [language_switcher_links] => Array ( [es] => /apache-arrow/cursos/guayaquil [en] => /en/apache-arrow/training/guayaquil ) [multi_lingual] => 1 ) ) ) [2] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => apache-arrow [1] => guayaquil ) ) [3] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /apache-arrow/cursos/guayaquil ) ) [4] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [5] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [6] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) Cursos de Apache Arrow en Guayaquil

Guayaquil - Mall del Sol

Mall del Sol, 4° Piso de las Torres del Centro Comercial, , Guayaquil , ecuador
El centro está en el cuarto piso de las Torres del Centro Comercial, la cuales constituyen el centro financiero y comercial ...

Descubra nuestros cursos

Apache Arrow for Data Analysis across Disparate Data Sources

 14 horas
Apache Arrow es un marco de procesamiento de datos en memoria de código abierto. A menudo se usa junto con otras herramientas de ciencia de datos para acceder a almacenes de datos dispares para su análisis. Se integra bien con otras tecnologías, como bases de datos de GPU , bibliotecas y herramientas de aprendizaje automático, motores de ejecución y marcos de visualización de datos. En esta capacitación en vivo dirigida por un instructor en el sitio, los participantes aprenderán cómo integrar Apache Arrow con varios marcos de Data Science para acceder a datos de fuentes de datos dispares. Al final de esta capacitación, los participantes podrán:
  • Instale y configure Apache Arrow en un entorno agrupado distribuido
  • Use Apache Arrow para acceder a datos de fuentes de datos dispares
  • Use Apache Arrow para evitar la necesidad de construir y mantener tuberías complejas de ETL
  • Analice datos a través de fuentes de datos dispares sin tener que consolidarlos en un repositorio centralizado.
Audiencia
  • Científicos de datos
  • Ingenieros de datos
Formato del curso
  • Conferencia parcial, discusión parcial, ejercicios y práctica práctica.
Nota
  • Para solicitar una capacitación personalizada para este curso, contáctenos para organizarlo.
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NP URI: www.nobleprog.com.ec/apache-arrow/cursos/guayaquil Undefined array key "nobleprog_site_production_url" /apps/nobleprog-website/includes/functions/new-modules-general-functions.php:82 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/new-modules-general-functions.php [line] => 82 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "nobleprog_site_production_url" [2] => /apps/nobleprog-website/includes/functions/new-modules-general-functions.php [3] => 82 ) ) [1] => Array ( [file] => /apps/hitra7/drupal7/sites/all/modules/_custom/frontend/islc7/islc7.module [line] => 131 [function] => variable_get [args] => Array ( [0] => nobleprog_site_production_url ) ) [2] => Array ( [file] => /apps/hitra7/drupal7/sites/all/modules/_custom/frontend/islc7/islc7.module [line] => 94 [function] => islc_get_current_site [args] => Array ( ) ) [3] => Array ( [file] => /apps/hitra7/drupal7/sites/all/modules/_custom/frontend/islc7/islc7_block.inc [line] => 34 [function] => islc_get_site_list [args] => Array ( ) ) [4] => Array ( [file] => /apps/nobleprog-website/nptemplates/default.php [line] => 265 [function] => islc7_sites_links_array_v3 [args] => Array ( ) ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 134 [args] => Array ( [0] => /apps/nobleprog-website/nptemplates/default.php ) [function] => require_once ) [6] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => apache-arrow [1] => guayaquil ) ) [7] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /apache-arrow/cursos/guayaquil ) ) [8] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [9] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [10] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/apache-arrow/cursos/guayaquil Undefined array key "devel_domain" /apps/nobleprog-website/includes/functions/new-modules-general-functions.php:82 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/new-modules-general-functions.php [line] => 82 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "devel_domain" [2] => /apps/nobleprog-website/includes/functions/new-modules-general-functions.php [3] => 82 ) ) [1] => Array ( [file] => /apps/hitra7/drupal7/sites/all/modules/_custom/frontend/islc7/islc7.module [line] => 99 [function] => variable_get [args] => Array ( [0] => devel_domain [1] => ) ) [2] => Array ( [file] => /apps/hitra7/drupal7/sites/all/modules/_custom/frontend/islc7/islc7_block.inc [line] => 34 [function] => islc_get_site_list [args] => Array ( ) ) [3] => Array ( [file] => /apps/nobleprog-website/nptemplates/default.php [line] => 265 [function] => islc7_sites_links_array_v3 [args] => Array ( ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 134 [args] => Array ( [0] => /apps/nobleprog-website/nptemplates/default.php ) [function] => require_once ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => apache-arrow [1] => guayaquil ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /apache-arrow/cursos/guayaquil ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.com.ec/apache-arrow/cursos/guayaquil Undefined array key "nobleprog_site_production_url" /apps/nobleprog-website/includes/functions/new-modules-general-functions.php:82 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/new-modules-general-functions.php [line] => 82 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "nobleprog_site_production_url" [2] => /apps/nobleprog-website/includes/functions/new-modules-general-functions.php [3] => 82 ) ) [1] => Array ( [file] => /apps/hitra7/drupal7/sites/all/modules/_custom/frontend/islc7/islc7.module [line] => 131 [function] => variable_get [args] => Array ( [0] => nobleprog_site_production_url ) ) [2] => Array ( [file] => /apps/hitra7/drupal7/sites/all/modules/_custom/frontend/islc7/islc7_block.inc [line] => 44 [function] => islc_get_current_site [args] => Array ( ) ) [3] => Array ( [file] => /apps/nobleprog-website/nptemplates/default.php [line] => 265 [function] => islc7_sites_links_array_v3 [args] => Array ( ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 134 [args] => Array ( [0] => /apps/nobleprog-website/nptemplates/default.php ) [function] => require_once ) [5] => Array ( [file] => /apps/nobleprog-website/modules/category_region/category_region.php [line] => 41 [function] => category_region_logic [args] => Array ( [0] => apache-arrow [1] => guayaquil ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => category_region_menu_callback [args] => Array ( [0] => /apache-arrow/cursos/guayaquil ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) )