Hadoop Developer

Apache Hadoop is the open source data management software that helps organizations analyze huge volumes of structured and unstructured data, is a very hot topic across the tech industry. It can be quickly learn to take advantage of the MapReduce framework through technical sessions and hands on labs.
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Experienced and certified trainers with real time project experience.


35-40 Interactive online training sessions.


In-house course content and with consistent course structure and design


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Weekly Assessments


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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.


Students must be belonging to IT Background and familiar with Concepts in Java and Linux.
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Introduction to Big data and Hadoop

  • Understanding Big Data
  • Challenges in processing Big Data
  • 3V Characteristics (Volume, Variety and Velocity)
  • Brief history of Hadoop
  • How Hadoop addresses Big Data?
  • HDFS and MR
  • Hadoop echo system

5 Daemons processes Map Reduce

  • Map Reduce Basics
  • Map Reduce Data Flow
  • Word count Example solving
  • Algorithms for simple and complex problems
  • Hadoop Streaming

HDFS (Hadoop Distributed File System)

  •  HDFS Overview and Architecture
  • HDFS Keywords like Name Node, Data
  • Node, Heart Beat etc
  • Configuring HDFS
  • Data Flows (Read and Write)
  • HDFS Permissions and Security
  • HDFS commands
  • Rack Awareness

Developing a Map Reduce Application

  • Setting up working environment
  • Custom Data types (Writable and Custom Key types)
  • Input and Output file formats
  • Driver, Mapper and Reducer Code Wal thru
  • Configuring IDE Eclipse
  • Writing Unit test and running locally
  • Map Reduce Web UI
  • Hands -on

How Map Reduce works?

  • Classic Map Reduce (Map Reduce I)
  • YARN (Map Reduce II)
  • Job Scheduling
  • Shuffle and Sort
  • Failures
  • Oozie Workflows
  • Hands-on Excercises

How Map Reduce works?

  • Map Reduce Types
  • Input formats – Input splits & records, text input, binary input, multiple inputs and database input.
  • Output formats – text output, binary output, multiple outputs, Lazy output and database output.
  • Hands-on

Hadoop Echo Systems PIG

  • Overview of PIG
  • Installation and running PIG
  • PIG Latin
  • Loading and storing data
  • Hands-on


  • Overview of HIVE
  • Installation and running HIVE
  • HiveQL
  • Tables
  • Hands-on


  • Overview of HBASE Installation  CLinets (avro, REST, Thrift)
  • Hands-on

Overview of SQOOP

  • Solving Case studies
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