About the training

This Hadoop training is designed to make you a certified Big Data practitioner by providing you rich hands-on training on Hadoop ecosystem and best practices about HDFS, MapReduce, HBase, Hive, Pig, Oozie, Sqoop. This course is stepping stone to your Big Data journey and you will get the opportunity to work on a Big data Analytics project after selecting a data-set of your choice. You will get Systems Domain Hadoop certification after the project completion.

 

Systems DomainTraining Objectives

The System Domain hadoop training is designed to help you become a top Hadoop developer. During this course, our expert instructors will train you to-

  • Master the concepts of HDFS and MapReduce framework
  • Understand Hadoop 2.x Architecture
  • Setup Hadoop Cluster and write Complex MapReduce programs
  • Learn data loading techniques using Sqoop and Flume
  • Perform data analytics using Pig, Hive and YARN
  • Implement HBase and MapReduce integration
  • Implement Advanced Usage and Indexing
  • Schedule jobs using Oozie
  • Implement best practices for Hadoop development
  • Understand Spark and its Ecosystem
  • Learn how to work in RDD in Spark

Why Big Data & Hadoop

Big Data & Hadoop Market is expected to reach $99.31B by 2022 growing at a CAGR of 42.1% from 2015 – Forbes McKinsey predicts that by 2018 there will be a shortage of 1.5M data experts – Mckinsey Report Avg salary of Big Data Hadoop Developers is $135k – Indeed.com Salary Data.

Who Should go for this Course

Market for Big Data analytics is growing across the world and this strong growth pattern translates into a great opportunity for all the IT Professionals.
Here are the few Professional IT groups, who are continuously enjoying the benefits moving into Big data domain:

  • Developers and Architects
  • BI /ETL/DW professionals
  • Senior IT Professionals
  • Testing professionals
  • Mainframe professionals
  • Freshers

Hadoop practitioners are among the highest paid IT professionals today with salaries ranging till $85K (source: indeed job portal), and the market demand for them is growing rapidly.

 

Prerequisite of the course

As such, there are no pre-requisites for learning Hadoop. Knowledge of Core Java and SQL will be beneficial, but certainly not a mandate. But If required by the candidates we can provide 2 weeks training on Core java and SQL before starting the Hadoop batch for them.

 

Course Curriculum and Learning Outcomes

Module Description Learning Outcomes

Big Data and Hadoop Concepts

Describe Big Data

List out the limitations of the existing solution

Define Hadoop and its components

Describe how Hadoop solves the limitations of the existing solution

Hadoop Architecture  

Explain HDFS Architecture

Describe the anatomy of File Read and Write

Explain MapReduce Process Flow

Hadoop Environment Set Up 

Define a Cluster

Describe different flavours of Hadoop

Execute the word count example

Hadoop MapReduce Concepts 

Compare and contrast traditional approach with Map Reduce way

Differentiate between Block and Split

Describe Combiners

Discuss Partitioners

Analytics Using Pig and Pig Latin 

Define Pig

Describe Pig Shell and Pig Operators

Discuss cases for using Pig

Execute Pig commands and operators in Pig

Shell  Define and discuss features of Hive

Analytics Using Hive  

Discuss use cases for Hive

Compare Hive and Pig, also Hive and RDBMS

Describe Hive components

Execute Hive queries

Identify existing data challenges

Analytics Using HBase  

Describe features of a likely solution

Define HBase

Explain data model in HBase architecture

Compare HBase and RDBMS

Hadoop 2.0 &  Identify challenges with Hadoop 1.0

Apache Oozie

Discuss solutions provided by Hadoop 2.0 in  terms of YARN

Explain Hadoop 2.0 Process Flow

Describe Apache OOzie as a scheduling service

 

Who are the instructors at Systems Domain

All the instructors at Systems Domain are practitioners from the Industry with minimum 10-12 yrs of relevant IT experience. They are subject matter experts and are trained by Systems Domain  for providing an awesome learning experience.

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