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Hadoop Training in Chennai

Learn how to use Hadoop from beginner level to advanced techniques which is taught by experienced working professionals. With our Hadoop Training in Chennai you’ll learn concepts in expert level with practical manner.

Hadoop is used to process massive volume of both structured and unstructured data that is so large that it’s difficult to process using traditional software techniques, When dealing with larger datasets, organizations face difficulties in being able to create, manipulate, and manage big data. Big data is particularly a problem in business analytics because standard tools and procedures are not designed to search and analyze massive datasets.

Hadoop is an open source implementation of Google file system and map reduce which is a batch processing system .Hadoop comprises not one but multiple components that compliment a complete software development life cycle. Hadoop popularity is due to fact that its ability to store, analyze and access large amounts of data, quickly and cost effectively across clusters of commodity hardware.

Hadoop’s major advantages are its ability to scale and deal with failures. Being a batch processing system, its can process a huge volume of data in the scales of petabytes. Hadoop is developed in java and has thrift API thus providing an ability to port with non-native languages like Python, Perl etc.

HDFS,MapReduce,Pig,Hive,Hbase are major components of Hadoop Stack, each play its own role in the Development cycle of a Hadoop Project.HDFS provide the storage space and Map reduce uses these data from HDFS to do computation. Pig and Hive are data access layers which provide defined ways to access and to perform data summarization using commands similar to SQL queries. Hbase is a column oriented NoSql database build on top hadoop disturbed file system, which provides high scalability and random read, writes access for applications. Since hadoop can easily integrated with cloud platforms like Amazon Ec2 and Rackspace, Organization can easily start up a hadoop implementation easily.

Course Name Hadoop Developer & Admin
Category Open-Source Software Framework (DWH)
Venue Greens Technologies
Official URL Hadoop Training
Demo Classes At Your Convenience
Training Methodology 10% Theory & 90% Practical
Course Duration 30-40 Hours
Class Availability Weekdays & Weekends
For Demo Class Email ID -

Hadoop Training Syllabus

Introduction to Hadoop

  • Hadoop Distributed File System
  • Hadoop Architecture
  • MapReduce & HDFS

Hadoop Eco Systems

  • Introduction to Pig
  • Introduction to Hive
  • Introduction to HBase
  • Other eco system Map

Hadoop Developer

  • Moving the Data into Hadoop
  • Moving The Data out from Hadoop
  • Reading and Writing the files in HDFS using java program
  • The Hadoop Java API for MapReduce
    • Mapper Class
    • Reducer Class
    • Driver Class
  • Writing Basic MapReduce Program In java
  • Understanding the MapReduce Internal Components
  • Hbase MapReduce Program
  • Hive Overview
  • Working with Hive
  • Pig Overview
  • Working with Pig
  • Sqoop Overview
  • Moving the Data from RDBMS to Hadoop
  • Moving the Data from RDBMS to Hbase
  • Moving the Data from RDBMS to Hive
  • Flume Overview
  • Moving The Data from Web server Into Hadoop
  • Real Time Example in Hadoop
  • Apache Log viewer Analysis
  • Market Basket Algorithms
  • Big Data Overview
  • Introduction In Hadoop and Hadoop Related Eco System.
  • Choosing Hardware For Hadoop Cluster nodes
  • Apache Hadoop Installation
    • Standalone Mode
    • Pseudo Distributed Mode
    • Fully Distributed Mode
  • Installing Hadoop Eco System and Integrate With Hadoop
    • Zookeeper Installation
    • Hbase Installation
    • Hive Installation
    • Pig Installation
    • Sqoop Installation
    • Installing Mahout
  • Horton Works Installation
  • Cloudera Installation
  • Hadoop Commands usage
  • Import the data in HDFS
  • Sample Hadoop Examples (Word count program and Population problem)
  • Monitoring The Hadoop Cluster
    • Monitoring Hadoop Cluster with Ganglia
    • Monitoring Hadoop Cluster with Nagios
    • Monitoring Hadoop Cluster with JMX
  • Hadoop Configuration management Tool
  • Hadoop Benchmarking

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