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Explore Our Courses

A Practical Introduction to Data Analysis and Big Data

 35 Hours
Participants who complete this instructor-led, live training in Quito will gain a practical, real-world understanding of Big Data and its related technologies, methodologies and tools. Participants will have the opportunity to put this knowledge into practice through hands-on exercises. Group interaction and instructor feedback make up an important component of the class. The course starts with an introduction to elemental concepts of Big Data, then progresses into the programming languages and methodologies used to perform Data Analysis. Finally, we discuss the tools and infrastructure that enable Big Data storage, Distributed Processing, and Scalability.
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Datameer for Data Analysts

 14 Hours
Datameer is a business intelligence and analytics platform built on Hadoop. It allows end-users to access, explore and correlate large-scale, structured, semi-structured and unstructured data in an easy-to-use fashion. In this instructor-led, live training, participants will learn how to use Datameer to overcome Hadoop's steep learning curve as they step through the setup and analysis of a series of big data sources. By the end of this training, participants will be able to:
  • Create, curate, and interactively explore an enterprise data lake
  • Access business intelligence data warehouses, transactional databases and other analytic stores
  • Use a spreadsheet user-interface to design end-to-end data processing pipelines
  • Access pre-built functions to explore complex data relationships
  • Use drag-and-drop wizards to visualize data and create dashboards
  • Use tables, charts, graphs, and maps to analyze query results
Audience
  • Data analysts
Format of the course
  • Part lecture, part discussion, exercises and heavy hands-on practice
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Big Data Analytics for Telecom Regulators

 14 Hours
To meet compliance of the regulators, CSPs (Communication service providers) can tap into Big Data Analytics which not only help them to meet compliance but within the scope of same project they can increase customer satisfaction and thus reduce the churn. In fact since compliance is related to Quality of service tied to a contract, any initiative towards meeting the compliance, will improve the “competitive edge” of the CSPs. Therefore, it is important that Regulators should be able to advise/guide a set of Big Data analytic practice for CSPs that will be of mutual benefit between the regulators and CSPs. The course consists of 8 modules (4 on day 1, and 4 on day 2)
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Big Data Business Intelligence for Govt. Agencies

 35 Hours
Advances in technologies and the increasing amount of information are transforming how business is conducted in many industries, including government. Government data generation and digital archiving rates are on the rise due to the rapid growth of mobile devices and applications, smart sensors and devices, cloud computing solutions, and citizen-facing portals. As digital information expands and becomes more complex, information management, processing, storage, security, and disposition become more complex as well. New capture, search, discovery, and analysis tools are helping organizations gain insights from their unstructured data. The government market is at a tipping point, realizing that information is a strategic asset, and government needs to protect, leverage, and analyze both structured and unstructured information to better serve and meet mission requirements. As government leaders strive to evolve data-driven organizations to successfully accomplish mission, they are laying the groundwork to correlate dependencies across events, people, processes, and information. High-value government solutions will be created from a mashup of the most disruptive technologies:
  • Mobile devices and applications
  • Cloud services
  • Social business technologies and networking
  • Big Data and analytics
IDC predicts that by 2020, the IT industry will reach $5 trillion, approximately $1.7 trillion larger than today, and that 80% of the industry's growth will be driven by these 3rd Platform technologies. In the long term, these technologies will be key tools for dealing with the complexity of increased digital information. Big Data is one of the intelligent industry solutions and allows government to make better decisions by taking action based on patterns revealed by analyzing large volumes of data — related and unrelated, structured and unstructured. But accomplishing these feats takes far more than simply accumulating massive quantities of data.“Making sense of thesevolumes of Big Datarequires cutting-edge tools and technologies that can analyze and extract useful knowledge from vast and diverse streams of information,” Tom Kalil and Fen Zhao of the White House Office of Science and Technology Policy wrote in a post on the OSTP Blog. The White House took a step toward helping agencies find these technologies when it established the National Big Data Research and Development Initiative in 2012. The initiative included more than $200 million to make the most of the explosion of Big Data and the tools needed to analyze it. The challenges that Big Data poses are nearly as daunting as its promise is encouraging. Storing data efficiently is one of these challenges. As always, budgets are tight, so agencies must minimize the per-megabyte price of storage and keep the data within easy access so that users can get it when they want it and how they need it. Backing up massive quantities of data heightens the challenge. Analyzing the data effectively is another major challenge. Many agencies employ commercial tools that enable them to sift through the mountains of data, spotting trends that can help them operate more efficiently. (A recent study by MeriTalk found that federal IT executives think Big Data could help agencies save more than $500 billion while also fulfilling mission objectives.). Custom-developed Big Data tools also are allowing agencies to address the need to analyze their data. For example, the Oak Ridge National Laboratory’s Computational Data Analytics Group has made its Piranha data analytics system available to other agencies. The system has helped medical researchers find a link that can alert doctors to aortic aneurysms before they strike. It’s also used for more mundane tasks, such as sifting through résumés to connect job candidates with hiring managers.
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Big Data Architect

 35 Hours
Day 1 - provides a high-level overview of essential Big Data topic areas. The module is divided into a series of sections, each of which is accompanied by a hands-on exercise. Day 2 - explores a range of topics that relate analysis practices and tools for Big Data environments. It does not get into implementation or programming details, but instead keeps coverage at a conceptual level, focusing on topics that enable participants to develop a comprehensive understanding of the common analysis functions and features offered by Big Data solutions. Day 3 - provides an overview of the fundamental and essential topic areas relating to Big Data solution platform architecture. It covers Big Data mechanisms required for the development of a Big Data solution platform and architectural options for assembling a data processing platform. Common scenarios are also presented to provide a basic understanding of how a Big Data solution platform is generally used.  Day 4 - builds upon Day 3 by exploring advanced topics relatng to Big Data solution platform architecture. In particular, different architectural layers that make up the Big Data solution platform are introduced and discussed, including data sources, data ingress, data storage, data processing and security.  Day 5 - covers a number of exercises and problems designed to test the delegates ability to apply knowledge of topics covered Day 3 and 4. 
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Big Data Business Intelligence for Criminal Intelligence Analysis

 35 Hours
Advances in technologies and the increasing amount of information are transforming how law enforcement is conducted. The challenges that Big Data pose are nearly as daunting as Big Data's promise. Storing data efficiently is one of these challenges; effectively analyzing it is another. In this instructor-led, live training, participants will learn the mindset with which to approach Big Data technologies, assess their impact on existing processes and policies, and implement these technologies for the purpose of identifying criminal activity and preventing crime. Case studies from law enforcement organizations around the world will be examined to gain insights on their adoption approaches, challenges and results. By the end of this training, participants will be able to:
  • Combine Big Data technology with traditional data gathering processes to piece together a story during an investigation
  • Implement industrial big data storage and processing solutions for data analysis
  • Prepare a proposal for the adoption of the most adequate tools and processes for enabling a data-driven approach to criminal investigation
Audience
  • Law Enforcement specialists with a technical background
Format of the course
  • Part lecture, part discussion, exercises and heavy hands-on practice
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Programming with Big Data in R

 21 Hours
Big Data is a term that refers to solutions destined for storing and processing large data sets. Developed by Google initially, these Big Data solutions have evolved and inspired other similar projects, many of which are available as open-source. R is a popular programming language in the financial industry.
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Big Data Storage Solution - NoSQL

 14 Hours
When traditional storage technologies don't handle the amount of data you need to store there are hundereds of alternatives. This course try to guide the participants what are alternatives for storing and analyzing Big Data and what are theirs pros and cons. This course is mostly focused on discussion and presentation of solutions, though hands-on exercises are available on demand.
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Big Data & Database Systems Fundamentals

 14 Hours
The course is part of the Data Scientist skill set (Domain: Data and Technology).
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Big Data - Data Science

 14 Hours
This classroom based training session will explore Big Data. Delegates will have computer based examples and case study exercises to undertake with relevant big data tools
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From Data to Decision with Big Data and Predictive Analytics

 21 Hours
Audience If you try to make sense out of the data you have access to or want to analyse unstructured data available on the net (like Twitter, Linked in, etc...) this course is for you. It is mostly aimed at decision makers and people who need to choose what data is worth collecting and what is worth analyzing. It is not aimed at people configuring the solution, those people will benefit from the big picture though. Delivery Mode During the course delegates will be presented with working examples of mostly open source technologies. Short lectures will be followed by presentation and simple exercises by the participants Content and Software used All software used is updated each time the course is run, so we check the newest versions possible. It covers the process from obtaining, formatting, processing and analysing the data, to explain how to automate decision making process with machine learning.
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Data Science for Big Data Analytics

 35 Hours
Big data is data sets that are so voluminous and complex that traditional data processing application software are inadequate to deal with them. Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating and information privacy.
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Machine Learning and Big Data

 7 Hours
This instructor-led, live training in Quito (online or onsite) is aimed at technical persons who wish to learn how to implement a machine learning strategy while maximizing the use of big data. By the end of this training, participants will:
  • Understand the evolution and trends for machine learning.
  • Know how machine learning is being used across different industries.
  • Become familiar with the tools, skills and services available to implement machine learning within an organization.
  • Understand how machine learning can be used to enhance data mining and analysis.
  • Learn what a data middle backend is, and how it is being used by businesses.
  • Understand the role that big data and intelligent applications are playing across industries.
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Sqoop and Flume for Big Data

 7 Hours
This instructor-led, live training in Quito (online or onsite) is aimed at software engineers who wish to use Sqoop and Flume for big data. By the end of this training, participants will be able to:
  • Ingest big data with Sqoop and Flume.
  • Ingest data from multiple data sources.
  • Move data from relational databases to HDFS and Hive.
  • Export data from HDFS to a relational database.
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Talend Big Data Integration

 28 Hours
This instructor-led, live training in Quito (online or onsite) is aimed at technical persons who wish to deploy Talend Open Studio for Big Data to simplifying the process of reading and crunching through Big Data. By the end of this training, participants will be able to:
  • Install and configure Talend Open Studio for Big Data.
  • Connect with Big Data systems such as Cloudera, HortonWorks, MapR, Amazon EMR and Apache.
  • Understand and set up Open Studio's big data components and connectors.
  • Configure parameters to automatically generate MapReduce code.
  • Use Open Studio's drag-and-drop interface to run Hadoop jobs.
  • Prototype big data pipelines.
  • Automate big data integration projects.
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Administrator Training for Apache Hadoop

 35 Hours
Audience: The course is intended for IT specialists looking for a solution to store and process large data sets in a distributed system environment Goal: Deep knowledge on Hadoop cluster administration.
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Big Data Analytics in Health

 21 Hours
Big data analytics involves the process of examining large amounts of varied data sets in order to uncover correlations, hidden patterns, and other useful insights. The health industry has massive amounts of complex heterogeneous medical and clinical data. Applying big data analytics on health data presents huge potential in deriving insights for improving delivery of healthcare. However, the enormity of these datasets poses great challenges in analyses and practical applications to a clinical environment. In this instructor-led, live training (remote), participants will learn how to perform big data analytics in health as they step through a series of hands-on live-lab exercises. By the end of this training, participants will be able to:
  • Install and configure big data analytics tools such as Hadoop MapReduce and Spark
  • Understand the characteristics of medical data
  • Apply big data techniques to deal with medical data
  • Study big data systems and algorithms in the context of health applications
Audience
  • Developers
  • Data Scientists
Format of the Course
  • Part lecture, part discussion, exercises and heavy hands-on practice.
Note
  • To request a customized training for this course, please contact us to arrange.
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Hadoop Administration

 21 Hours
The course is dedicated to IT specialists that are looking for a solution to store and process large data sets in distributed system environment Course goal: Getting knowledge regarding Hadoop cluster administration
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Hadoop For Administrators

 21 Hours
Apache Hadoop is the most popular framework for processing Big Data on clusters of servers. In this three (optionally, four) days course, attendees will learn about the business benefits and use cases for Hadoop and its ecosystem, how to plan cluster deployment and growth, how to install, maintain, monitor, troubleshoot and optimize Hadoop. They will also practice cluster bulk data load, get familiar with various Hadoop distributions, and practice installing and managing Hadoop ecosystem tools. The course finishes off with discussion of securing cluster with Kerberos. “…The materials were very well prepared and covered thoroughly. The Lab was very helpful and well organized”
— Andrew Nguyen, Principal Integration DW Engineer, Microsoft Online Advertising Audience Hadoop administrators Format Lectures and hands-on labs, approximate balance 60% lectures, 40% labs.
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Hadoop for Developers (4 days)

 28 Hours
Apache Hadoop is the most popular framework for processing Big Data on clusters of servers. This course will introduce a developer to various components (HDFS, MapReduce, Pig, Hive and HBase) Hadoop ecosystem.  
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Advanced Hadoop for Developers

 21 Hours
Apache Hadoop is one of the most popular frameworks for processing Big Data on clusters of servers. This course delves into data management in HDFS, advanced Pig, Hive, and HBase.  These advanced programming techniques will be beneficial to experienced Hadoop developers. Audience: developers Duration: three days Format: lectures (50%) and hands-on labs (50%).  
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Hadoop for Developers and Administrators

 21 Hours
Hadoop is the most popular Big Data processing framework.
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Hadoop for Project Managers

 14 Hours
In this instructor-led training in Quito, participants will learn the core components of the Hadoop ecosystem and how these technologies can be used to solve large-scale problems. By learning these foundations, participants will  improve their ability to communicate with the developers and implementers of these systems as well as the data scientists and analysts that many IT projects involve. Audience
  • Project Managers wishing to implement Hadoop into their existing development or IT infrastructure
  • Project Managers needing to communicate with cross-functional teams that include big data engineers, data scientists and business analysts
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Hadoop Administration on MapR

 28 Hours
Audience: This course is intended to demystify big data/hadoop technology and to show it is not difficult to understand.
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Hadoop with Python

 28 Hours
Hadoop is a popular Big Data processing framework. Python is a high-level programming language famous for its clear syntax and code readibility. In this instructor-led, live training, participants will learn how to work with Hadoop, MapReduce, Pig, and Spark using Python as they step through multiple examples and use cases. By the end of this training, participants will be able to:
  • Understand the basic concepts behind Hadoop, MapReduce, Pig, and Spark
  • Use Python with Hadoop Distributed File System (HDFS), MapReduce, Pig, and Spark
  • Use Snakebite to programmatically access HDFS within Python
  • Use mrjob to write MapReduce jobs in Python
  • Write Spark programs with Python
  • Extend the functionality of pig using Python UDFs
  • Manage MapReduce jobs and Pig scripts using Luigi
Audience
  • Developers
  • IT Professionals
Format of the course
  • Part lecture, part discussion, exercises and heavy hands-on practice
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Hadoop and Spark for Administrators

 35 Hours
This instructor-led, live training in Quito (online or onsite) is aimed at system administrators who wish to learn how to set up, deploy and manage Hadoop clusters within their organization. By the end of this training, participants will be able to:
  • Install and configure Apache Hadoop.
  • Understand the four major components in the Hadoop ecoystem: HDFS, MapReduce, YARN, and Hadoop Common.
  • Use Hadoop Distributed File System (HDFS) to scale a cluster to hundreds or thousands of nodes.  
  • Set up HDFS to operate as storage engine for on-premise Spark deployments.
  • Set up Spark to access alternative storage solutions such as Amazon S3 and NoSQL database systems such as Redis, Elasticsearch, Couchbase, Aerospike, etc.
  • Carry out administrative tasks such as provisioning, management, monitoring and securing an Apache Hadoop cluster.
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HBase for Developers

 21 Hours
This course introduces HBase – a NoSQL store on top of Hadoop.  The course is intended for developers who will be using HBase to develop applications,  and administrators who will manage HBase clusters. We will walk a developer through HBase architecture and data modelling and application development on HBase. It will also discuss using MapReduce with HBase, and some administration topics, related to performance optimization. The course  is very  hands-on with lots of lab exercises.
Duration : 3 days Audience : Developers  & Administrators
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Apache NiFi for Administrators

 21 Hours
In this instructor-led, live training in Quito (onsite or remote), participants will learn how to deploy and manage Apache NiFi in a live lab environment. By the end of this training, participants will be able to:
  • Install and configure Apachi NiFi.
  • Source, transform and manage data from disparate, distributed data sources, including databases and big data lakes.
  • Automate dataflows.
  • Enable streaming analytics.
  • Apply various approaches for data ingestion.
  • Transform Big Data and into business insights.
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Apache NiFi for Developers

 7 Hours
In this instructor-led, live training in Quito, participants will learn the fundamentals of flow-based programming as they develop a number of demo extensions, components and processors using Apache NiFi. By the end of this training, participants will be able to:
  • Understand NiFi's architecture and dataflow concepts.
  • Develop extensions using NiFi and third-party APIs.
  • Custom develop their own Apache Nifi processor.
  • Ingest and process real-time data from disparate and uncommon file formats and data sources.
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Samza for Stream Processing

 14 Hours
Apache Samza is an open-source near-realtime, asynchronous computational framework for stream processing.  It uses Apache Kafka for messaging, and Apache Hadoop YARN for fault tolerance, processor isolation, security, and resource management. This instructor-led, live training introduces the principles behind messaging systems and distributed stream processing, while walking participants through the creation of a sample Samza-based project and job execution. By the end of this training, participants will be able to:
  • Use Samza to simplify the code needed to produce and consume messages.
  • Decouple the handling of messages from an application.
  • Use Samza to implement near-realtime asynchronous computation.
  • Use stream processing to provide a higher level of abstraction over messaging systems.
Audience
  • Developers
Format of the course
  • Part lecture, part discussion, exercises and heavy hands-on practice
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Testimonials (25)

I liked the virtual machine environments because he could easily toggle between the views and help if we were struggling with the material.

Pedro
Course - Apache NiFi for Developers

The training was conducted in an interesting and professional way, which allowed for systematization and expansion of knowledge in the field of the training topic. The presenter demonstrated extensive experience and the ability to convey information. The training was very practical and tailored to our needs. I recommend

Dominik Kozłowski - Shell Polska
Course - Big Data - Data Science

Machine Translated

How big data work, data programs, greater knowledge of how our current world works using data

Ozayr Hussain - Vodacom
Course - A Practical Introduction to Data Analysis and Big Data

Commitment and willingness to explain secondary topics.

Marek - Krajowy Rejestr Długów Biuro Informacji Gospodarczej S.A.
Course - Apache Spark Fundamentals

Machine Translated

The fact that we were able to take with us most of the information/course/presentation/exercises done, so that we can look over them and perhaps redo what we didint understand first time or improve what we already did.

Raul Mihail Rat - Accenture Industrial SS
Course - Python, Spark, and Hadoop for Big Data

Muy poco, se me dificulto mucho y mas por que entre desfasado, no tome los primeras sesiones.

Rolando García - OIT para México y Cuba
Course - Apache NiFi for Administrators

La combinación de teoría y práctica con herramientas como databricks

Graciela Saud - Servicio de Impuestos Internos
Course - Spark for Developers

how the trainor shows his knowledge in the subject he's teachign

john ernesto ii fernandez - Philippine AXA Life Insurance Corporation
Course - Data Vault: Building a Scalable Data Warehouse

very interactive...

Richard Langford
Course - SMACK Stack for Data Science

Sufficient hands on, trainer is knowledgable

Chris Tan
Course - A Practical Introduction to Stream Processing

I learned a lot - not only in theoretical knowledge but I also applied that knowledge during the training and therefore I really understood what process mining is and how it works. Thanks a lot!

Julia Dörre - Techniker Krankenkasse
Course - Process Mining

Having hands on session / assignments

Poornima Chenthamarakshan - Intelligent Medical Objects
Course - Apache Spark in the Cloud

Very useful in because it helps me understand what we can do with the data in our context. It will also help me

Nicolas NEMORIN - Adecco Groupe France
Course - KNIME Analytics Platform for BI

Intensity, Training materials and expertise, Clarity, Excellent communication with Alessandra

Marija Hornis Dmitrovic - Marija Hornis
Course - Data Science for Big Data Analytics

I feel more confident with coding now. I've never done it before but now I understand that it's not rocket science and I can do it when necessary.

Anna - Birmingham City University
Course - Foundation R

practical things of doing, also theory was served good by Ajay

Dominik Mazur - Capgemini Polska Sp. z o.o.
Course - Hadoop Administration on MapR

The VM I liked very much The Teacher was very knowledgeable regarding the topic as well as other topics, he was very nice and friendly I liked the facility in Dubai.

Safar Alqahtani - Elm Information Security
Course - Big Data Analytics in Health

I generally liked the fernando's knowledge.

Valentin de Dianous - Informatique ProContact INC.
Course - Big Data Architect

The content, as I found it very interesting and think it would help me in my final year at University.

Krishan - NBrown Group
Course - From Data to Decision with Big Data and Predictive Analytics

The subject matter and the pace were perfect.

Tim - Ottawa Research and Development Center, Science Technology Branch, Agriculture and Agri-Food Canada
Course - Programming with Big Data in R

I enjoyed the good real world examples, reviews of existing reports.

Ronald Parrish
Course - Data Visualization

I genuinely enjoyed the many hands-on sessions.

Jacek Pieczątka
Course - Administrator Training for Apache Hadoop

Very tailored to needs.

Yashan Wang
Course - Data Mining with R

The trainer was so knowledgeable and included areas I was interested in.

Mohamed Salama
Course - Data Mining & Machine Learning with R

The fact that all the data and software was ready to use on an already prepared VM, provided by the trainer in external disks.

vyzVoice
Course - Hadoop for Developers and Administrators

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