Elegant It Services Offering Hadoop Training for freshers and Experienced professionals

                        

Hadoop Developer Training

Introduction

Elegant IT Services, a Bay Area startup, provides professional Big Data services and facilitates the extraction of nuggets of information to support data-based decision making. We are Hadoop specialists and our specially trained professionals help our client’s tame Big Data challenges.

Course Objective Summary

During this course, you will learn:

• Introduction to Big Data and Hadoop

• Hadoop ecosystem - Concepts

• Hadoop Map-reduce concepts and features

• Developing the map-reduce Applications

• Pig concepts

• Hive concepts

• HBASE Concepts

• Mongo DB Concepts

• Sqoop Concepts

• Real Life Use Cases

Introduction to Big Data and Hadoop

• What is Big Data?

• What are the challenges for processing big data?

• What technologies support big data?

• What is Hadoop?

• Why Hadoop?

• History of Hadoop

• Use Cases of Hadoop

• Hadoop eco System

• HDFS

• Map Reduce

• Statistics

Understanding the Cluster

• Typical workflow

• Writing files to HDFS

• Reading files from HDFS

• Rack Awareness

• 5 daemons

Let's talk Map Reduce

• Before Map reduce

• Map Reduce Overview

• Word Count Problem

• Word Count Flow and Solution

• Map Reduce Flow

• Algorithms for simple problems

• Algorithms for complex problems

Developing the Map Reduce Application

• Data Types

• File Formats

• Explain the Driver, Mapper and Reducer code

• Configuring development environment - Eclipse

• Writing Unit Test

• Running locally

• Running on Cluster

• Hands on exercises

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• Anatomy of Map Reduce Job run

• Job Submission

• Job Initialization

• Task Assignment

• Job Completion

• Job Scheduling

• Job Failures

• Shuffle and sort

• Oozie Workflows

• Hands on Exercises

Map Reduce Types and Formats

• MapReduce Types

• Input Formats - Input splits & records, text input,binary input, multiple inputs & database input.

• Output Formats - text Output, binary output, multiple outputs, lazy output and database output

• Hands on Exercises.

Map Reduce Features

• Counters

• Sorting

• Joins - Map Side and Reduce Side

• Side Data Distribution

• MapReduce Combiner

• MapReduce Partitioner

• MapReduce Distributed Cache

• Hands Exercises

Hive and PIG

• Fundamentals

• When to Use PIG and HIVE

• Concepts

• Hands on Exercises

HBASE

• CAP Theorem

• Hbase Architecture and concepts

• Programming and Hands on Exercises

Case Studies Discussions