HADOOP

Overview:
Hadoop is a framework for distributed computation and storage of very large data sets on computer clusters. Hadoop has three core components are Hadoop Distributed File System (HDFS), MapReduce, Yet another Resource Negotiator (YARN). Hadoop is a free, Java-based programming framework that supports the processing of large data sets in a distributed computing environment. Hadoop makes it possible to run applications on systems with thousands of nodes involving thousands of terabyte.

Training Objectives of Hadoop:
Big Data is fastest growing and most promising technology for handling large volumes of data for doing data analytics. This Big Data Hadoop training will help you tobe up and running in the most demanding professional skills. Almost all the top MNC are trying to get into Big Data Hadoop hence there is a huge demand for Certified Big Data professionals. Our Big Data online training will help you to upgrade your career in big data domain.

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 Counters
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
Customized InputFormats 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.