hadoop training institute from marathalli

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