Hadoop Online Training

Duration of       Hours 


Duration time may vary depends on course progress


Hadoop Online Training

Training Objectives of Hadoop:

Hadoop Course will provide the basic concepts of MapReduce applications developed using Hadoop, including a close look at framework components, use of Hadoop for a variety of data analysis tasks, and numerous examples of Hadoop in action. This course will further examine related technologies such as Hive, Pig, and Apache Accumulo.

Target Students / Prerequisites:

Students must be belonging to IT Background and familiar with Concepts in Java and Linux.



Course Content

Introduction, The Motivation for Hadoop:

  • Problems with traditional large-scale systems

  • Requirements for a new approach

Hadoop Basic Concepts:

  • An Overview of Hadoop

  • The Hadoop Distributed File System

  • Hands-on Exercise

  • How MapReduce Works

  • Hands-on Exercise

  • Anatomy of a Hadoop Cluster

  • Other Hadoop Ecosystem Components

Writing a MapReduce Program:

  • Examining a Sample MapReduce Program

  • With several examples

  • Basic API Concepts

  • The Driver Code

  • The Mapper

  • The Reducer

  • Hadoop’s Streaming API

Delving Deeper Into The Hadoop API:

  • More About ToolRunner

  • Testing with MRUnit

  • Reducing Intermediate Data With Combiners

  • The configure and close methods for Map/Reduce Setup and Teardown

  • Writing Partitioners for Better Load Balancing

  • Hands-On Exercise

  • Directly Accessing HDFS

  • Using the Distributed Cache

  • Hands-On Exercise

Performing several Hadoop jobs:

  • The configure and close Methods

  • Sequence Files

  • Record Reader

  • Record Writer

  • Role of Reporter

  • Output Collector

  • Processing video files and audio files

  • Processing image files

  • Processing XML files

  • Counters

  • Directly Accessing HDFS

  • ToolRunner

  • Using The Distributed Cache

Common MapReduce Algorithms:

  • Sorting and Searching

  • Indexing

  • Classification/Machine Learning

  • Term Frequency-Inverse Document Frequency

  • Word Co-Occurrence

  • Hands-On Exercise: Creating an Inverted Index

  • Identity Mapper

  • Identity Reducer

  • Exploring well known problems using MapReduce applications

Using HBase:

  • What is HBase?

  • HBase API

  • Managing large data sets with HBase

  • Using HBase in Hadoop applications

  • Hands-on Exercise

Using Hive and Pig:

  • Hive Basics

  • Pig Basics

  • Hands-on Exercise

  • Practical Development Tips and Techniques

  • Debugging MapReduce Code

  • Using LocalJobRunner Mode for Easier Debugging

  • Retrieving Job Information with Countries

  • Logging

  • Splittable File Formats

  • Determining the Optimal Number of Reducers

  • Map-Only MapReduce Jobs

  • Hands-on Exercise

Debugging MapReduce Programs:

  • Testing with MRUnit

  • Logging

  • Classification/Machine Learning

  • Advanced MapReduce Programming

  • A Recap of the MapReduce Flow

  • The Secondary Sort

  • CustomizedInputFormats and OutputFormats

  • Pipelining Jobs With Oozie

  • Map-Side Joins

  • Reduce-Side Joins

Joining Data Sets in MapReduce:

  • Map-Side Joins

  • The Secondary Sort

  • Reduce-Side Joins

Monitoring and debugging on a Production Cluster:

  • Counters

  • Skipping Bad Records

  • Rerunning failed tasks with Isolation Runner

Tuning for Performance in MapReduce:

  • Reducing network traffic with combiner

  • Partitioners

  • Reducing the amount of input data

  • Using Compression

  • Reusing the JVM

  • Running with speculative execution

  • Refactoring code and rewriting algorithms Parameters affecting Performance

  • Other Performance Aspects

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