Big Data Training

Big Data Training and Courseware

The data is considered in the Big Data category when traditional systems and tools (e.g. databases, OLAP and data-mining systems used in data marts or warehouses) may become either prohibitively expensive to handle the exponential growth of data volumes or found unsuitable for the job.

Most organizations use just a fraction of the data available to them as it is either too expensive to process it or business has no expertise to extract the relevant information. Businesses that effectively leverage Big Data (that was originally discarded or not processed due to technology limitations) get a competitive advantage over their competitors. Insights from Big Data help improve services and products, develop deeper customer relationships in a more agile and predictive manner and uncover new monetization opportunities.

Related course categories

Hadoop
NoSQL
Data Science

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Our courses

WA2726 AWS Advanced Analytics for Structured Data

This 2 day course provides a technical introduction to the understanding, creation and digital data supply chains for advanced analytics with AWS.

WA2713 Artificial Intelligence for Managers

Machine Learning can help businesses reengineer their processes for higher revenue, higher customer satisfaction and lower cost. This course teaches the fundamentals of machine learning and how it differs from traditional rule based software. It then proceeds to give definitive real world guidance on how businesses can adopt some of the algorithms to improve their performance.

WA2711 R Programming from the Ground Up

Over the past few years, R has been steadily gaining popularity with business analysts, statisticians and data scientists as a tool of choice for conducting statistical analysis of data as well as supervised and unsupervised machine learning.

WA2700 Introduction to Talend

This 3 day course provides an introduction to Talend.

WA2688 Data Science and Big Data Analytics

This intensive training course provides theoretical and technical aspects of Data Science and Business Analytics.  The course covers the fundamental and advanced concepts and methods of deriving business insights from big” and/or “small” data.  This training course is supplemented by hands-on labs that help attendees reinforce their theoretical knowledge of the learned material.

WA2610 Machine Learning with Apache Spark

To stay competitive, organizations have started adopting new approaches to data processing and analysis.  For example, data scientists are turning to Apache Spark for processing massive amounts of data using Apache Spark’s distributed compute capability and its built-in machine learning library.

This intensive Apache Spark training course provides an overview of data science algorithms as well as the theoretical and technical aspects of using the Apache Spark platform for Machine Learning.  This training course is supplemented by a variety of hands-on labs that help attendees reinforce their theoretical knowledge of the learned material.

WA2592 Applied Data Science

This intensive training course provides theoretical and technical aspects of Data Science and Business Analytics.  The course covers the fundamental and advanced concepts and methods of deriving business insights from big” and/or “small” data.  This training course is supplemented by hands-on labs that help attendees reinforce their theoretical knowledge of the learned material.

TP2424 Hadoop Training: Administering Hadoop

This 5 day training course provides System Administrators with a detailed understanding of all the skills required to operate and manage Hadoop clusters. It covers Installation, Configuration, Monitoring and Performance Tuning of Hadoop clusters in diversified Environments.

WA2393 Big Data Training: Data Science for Solution Architects

This big data training course helps Solution Architects and other IT practitioners understand the value proposition, methodology and techniques of the emerging discipline of Data Science.  The class also introduces the students to a number of existing production-ready technologies and capabilities that enable enterprises to build cost-efficient Big Data processing solutions.

WA2342 NoSQL Architecture Comparison

The NoSQL (Not Only SQL) persistence systems space offers a great variety of solutions that may be overwhelming.  This class aims at helping the attendees  understand the challenges of the emerging world of Big Data as well as identify suitable use cases for a variety of NoSQL systems such as Pig, Hive, HBase, Cassandra and MongoDB.

The attendees will also be given some underlying architecture details of those NoSQL systems to enable them make informed decisions about using NoSQL systems when they return to work.

WA2341 APACHE HADOOP TRAINING: Hadoop Programming on the Cloudera Platform

This training course introduces the students to Apache Hadoop and key Hadoop ecosystem projects: Pig, Hive, Sqoop, Impala, Oozie, HBase, and Spark.

WA2268 Big Data and NoSQL for Developers

This course provides application developers with technical overview of Big Data as well as NoSQL (Not Only SQL) database systems.  Effective use of NoSQL systems and understanding the appropriate ways of handling Big Data leads to the creation of the next-generation of high-performance and robust solutions.

WA2267 Big Data Management Solutions for Architects

Many organizations are overwhelmed by the sheer volume of information they have to process in order to stay competitive.  Traditional database systems may become either prohibitively expensive to handle the exponential growth of data volumes or found unsuitable for the job.  Finding solutions to meet the challenges posed by  Big Data requires, among other things, understanding the value proposition of NoSQL systems and the Cloud as well new techniques of data processing.

WA2266 Development with MongoDB

MongoDB is an open source document-oriented NoSQL (Not Only SQL) database written in C++.  Effective use of MongoDB, understanding its data structures and optimal ways to program to its API aids in creating high-performance and robust solutions in small start-ups and big companies alike.

WA2192 Introduction to Big Data and NoSQL

We live in the information age where business success is grounded on the ability of organizations to convert raw data coming from various sources into high-grade business information.

Many organizations are overwhelmed by the sheer volume of information they have to process in order to stay competitive.  Traditional database systems may become either prohibitively expensive to handle the exponential growth of data volumes or found unsuitable for the job.  At this point, the data gets mystically morphed into the Big Data.

This course provides an introduction to Big Data as well as NoSQL (Not Only SQL) database systems.  The fundamental concepts of and ideas behind Big Data / NoSQL technologies are methodically explored and many buzzwords demystified.  The course is supplemented by hands-on labs that help attendees reinforce their theoretical knowledge of the subject.

WA2186 Big Data and Analytics for Business Users

Data is one of the most valuable assets that your organization possesses.  Every day you are creating more data and potentially passing up opportunities to harvest that data and use it to accelerate the achievement of your organization’s strategic objectives.  Big Data and Analytics represent an emerging trend around harvesting, analyzing, and capitalizing on the wealth of data that is within the grasp of your enterprise.

Frequently Asked Questions:

What is Big Data?

Gartner defines Big Data as “Big data are high volume, high velocity, and/or high variety information assets that require new forms of processing to enable enhanced decision making, insight discovery, and process optimization.”

Put simply, “big data” describes huge amounts of information that is easy to obtain, but so massive that they challenge current computing technologies

Big data is the problem you have when you have information coming in from multiple sources (computers, satellites, mobile devices, cameras, microphones, and more). That information needs to be moved around, stored (we’re talking petabytes and exabytes, for example), and processed.

How is Big Data collected?

Big Data can come from many different sources such as computers, satellites, mobile devices, cameras, microphones, and more. It can be collected through social media or through open data sources. It can involve multiple, simultaneous data sources, which may not otherwise be integrated

Big Data can exist in a wide variety of file types, including structured data, such as SQL database stores, or unstructured data, such as document files or streaming data

What can Big Data do?

Most organizations use just a fraction of the data available to them as it is either too expensive to process it or business has no expertise to extract the relevant information.

Businesses that effectively leverage Big Data (that was originally discarded or not processed due to technology limitations) get a competitive advantage over their competitors. Insights from Big Data help improve services and products, develop deeper customer relationships in a more agile and predictive manner and uncover new monetization opportunities.

Since storage costs of Big Data in many cases is not an issue, businesses may request their IT to extend retention period of some data feeds and come up with usage ideas later on. Specialized Big Data solutions can offer real or near real-time analytics. Overall, with Big Data, business agility is achieved and new features can be incorporated into applications quickly and easily.

How is Big Data Used?

See the answer to the above question.

When is Big Data used?

As per Gartner’s definition, the three defining properties to Big Data are high volume, high velocity, and/or high variety information assets that require new forms of processing to enable enhanced decision making, insight discovery and process optimization

How do Big Data and Hadoop relate?

Hadoop is a distributed fault-tolerant computing platform written in Java. It’s modeled after shared-nothing, massively parallel processing (MPP) system design. Hadoop’s design was influenced by ideas published in Google File System (GFS) and MapReduce white papers. Hadoop’s core component, Hadoop Distributed File System (HDFS) is the counterpart of GFS. Hadoop uses functionally equivalent to Google’s MapReduce data processing system also called MapReduce (term coined by Google’s engineers). Hadoop is written in Java to ensure that HDFS is portable. One of the main focuses of Hadoop’s architecture was to “design for failure”.

Top facts on Big Data

According to Forbes magazine:

For a typical Fortune 1000 company, just a 10% increase in data accessibility will result in more than $65 million additional net income. Estimates suggest that by better integrating big data, healthcare could save as much as $300 billion a year — that’s equal to reducing costs by $1000 a year for every man, woman, and child. Retailers who leverage the full power of big data could increase their operating margins by as much as 60%.