:  +91 8099 133 133 (India)


:  +91 8099 133 133 (India)

Course Description

This course teaches you key concepts and expertise necessary to create robust data processing applications using Apache Hadoop. It covers the complete Hadoop ecosystem topics such as:

  • The internals of MapReduce and HDFS and how to write MapReduce code
  • Master the concepts of Hadoop Distributed File System and MapReduce framework
  • Best practices for Hadoop development, debugging, and implementation of workflows and common algorithms
  • How to leverage Hive, Pig, Sqoop, Flume, Oozie, and other Hadoop ecosystem projects
  • Creating custom components such as WritableComparables and InputFormats to manage complex data types
  • Writing and executing joins to link data sets in MapReduce
  • Advanced Hadoop API topics required for real-world data analysis
  • Learn to write Complex MapReduce programs
  • Program in YARN.
  • Perform Data Analytics using Pig and Hive
  • Implement HBase, MapReduce Integration, Advanced Usage and Advanced Indexing.
  • Schedule jobs using Oozie. 
  • Implement best Practices for Hadoop Development
  • Implement a Hadoop Project
  • Implement some Real world projects on Big Data Analytics
  • Some prior experience in Core Java and good analytical skills
  • Basic knowledge of unix , sql scripting
  • Prior knowledge of Apache Hadoop is not required.

Why Learn Hadoop developer :

Hadoop is a combination of online running applications on a very huge scale built of commodity hardware. Hadoop is uncluttered source software which is handled by the Apache Software Foundation and it’s very helpful in storing and handling huge amounts of data inexpensively and professionally. Basically Hadoop collects huge packets of data and classifies this data using MapReduce.

If you are looking for Hadoop Jobs and you are a Hadoop professional then there are a lot of jobs about Hadoop and related technologies. There are many companies like Google, Yahoo, Apple, Hortonworks, ebaY, Facebook, ORACLE, IBM, Microsoft, and CISCO which are looking for skilled professionals having experience in this field and are capable of managing the Big Data in their companies. If you are a professional of Hadoop then you could be one of them. These companies such as Google, Facebook, and ORACLE etc are looking for the Hadoop Professionals at different levels such as database Administrators, Hadoop Professionals having complete operational skills, Hadoop engineers & also senior Hadoop engineers, big data Engineers, Hadoop developers and also Java Engineers (DSE Team).

Research of IDC shows that the Big Data market revenue’s will grow at 31.7 percent a year and it will hit the $23.8 billion mark in 2016. According to the latest research by market the Hadoop and Big Data world widely is expected to growth about 13.9$ billion by 2017.

Hadoop Developers: Hadoop Developer is a person who just loves programming and he must have knowledge about Core, Jave, SQL and other languages along with remarkable skills.

Companies Using Hadoop:

Amazon Web Services, IBM, Hortonworks, Cloudera, Intel, Microsoft, Pivotal, Twitter, Salesforce, AT&T Stumbleupon, Ebay, Yahoo, Facebook , Hulu etc.

Career Opportunities after Hadoop course:

Google trends tells exponential growth of Jobs in Hadoop. Check Top Job websites for Hadoop Jobs:

Indeed :11000+

Simplyhired: 12000+

LikedIn: 4500+ 8000+

Rating: 7.6/10- 82 reviews
Course Contents

Object Oriented Programming

  • Concepts of Object Oriented Programming
  • Class, Object, Data Abstraction, Data Encapsulation, Inheritance and Polymorphism
  • Identifying Real world examples


Core Java

  • Overview of Java, Environment setup
  • Basic Syntax, Objects and Classes
  • Basic Data types, Variable types and Modifier types
  • Basic Operators, Loop control and Decision making
  • Methods
  • Implementing Abstractions, Encapsulation, Inheritance and Polymorphism
  • Characters, String, Arrays and
  • Packages, Interfaces, Collections, Generics and Basics of Java design patterns
  • Files and I/O
  • Serialization
  • Exceptions
  • RegEx


Introduction to the Linux Operating System

  • UNIX Commands and Concepts


Introduction to Big Data

  • What is Big Data? Limitations of existing Solutions for Big data.
  • What is Hadoop? Advantages and Disadvantages
  • History of Hadoop
  • Why Hadoop and its Real Time Use cases?
  • Where exactly Hadoop fits into Application Architecture World?
  • Different Core Components of Hadoop 2.x and Hadoop Architecture
  • Different Ecosystems of Hadoop


Hadoop Architecture

  • What is Cluster? What is node?
  • Hadoop 2.x Cluster Architecture – Federation and High Availability
  • Cluster Modes
  • Common Configuration files



  • What is HDFS? HDFS Architecture
  • Features of HDFS
  • Hadoop daemons and its functionalities
  • Introduction to Data Storage – Blocks and Data Replication
  • Accessing HDFS – CLI, Admin commands and Java based approach


Hadoop MapReduce

  • Why MapReduce? Traditional way vs MapReduce way
  • Hadoop 2.x MapReduce Architecture and its Components
  • YARN MR Application Execution Flow and YARN Workflow
  • Anatomy of MapReduce program
  • Basics of MapReduce – Driver, Mapper and Reducer, Input Splits, Relation between Input Splits and HDFS Blocks
  • Writing basic MR Job, Running MR jobs in Local mode and Distributed mode
  • Data types in MapReduce
  • Input Formatters and its associated Record Readers
    • Text Input Formatter
    • Key Value Text Input Formatter
    • Sequence File Input Formatter
    • Writing Custom Input Formatters and Its Record Readers
  • Output Formatters and its associated Record Writers Combiners (Mini Reducers), Partitioners and uses of them
    • Text Output Formatter
    • Sequence File Output Formatter
    • Writing Custom Output Formatters and Its Record Writers
  • Importance of Distributed Cache, Counters and how to use Counters
  • MR Joins – Map Side and Reducer Side
  • Use of Secondary Sorting Use of Compression Techniques – Snappy, LZO and Zip
    • Importance of Writable and Writable Comparable APIs
    • Writing MR Keys and Values
  • Schedulers and Importance of Schedulers – FIFO, Capacity and Fair
  • Debug MR Jobs in Local and Pseudo Cluster modes
  • MR Streaming and Pipelining

Identifying Performance Bottlenecks and fine tuning MR Jobs



  • Introduction to Apache Pig
  • MapReduce vs Apache Pig, SQL vs Apache Pig
  • Types of Use Cases where we can use Apache Pig
  • Different data types in Pig and its Limitations
  • Pig Latin Programming Structure
  • Modes of Execution in PigTransformations in Pig
    • Grunt Shell
    • Script
    • Embedded
  • UDFs in Pig 



  • Introduction, Use cases where Hive can be used and its Limitations
  • Hive Architecture and Components – Driver, Compiler and Semantic Analyzer
  • Installation and Configuration of Hive on Single node and Multi node Cluster
  • Hive Integration with Hadoop
  • Hive Query Language (Hive QL)
  • SQL vs Hive QL
  • Hive Execution – MapR and Local mode
  • Hive DLL and DML operations
  • Hive MetastoreHive Data types and Data Models
    • Embedded Metastore Configuration
    • External Metastore Configuration
  • Partitions and Buckets
  • Hive TablesImporting, Querying and Managing Data
    • Managed Tables
    • External Tables
  • Hive Script
  • Hive UDFs  and Hive UDAFs
  • Hive SerDe



  • HBase Introduction, How HBase worksUse cases where HBase can be used
    • Column Families and Scans
  • HBase Installation
  • HBase ArchitectureHBase Introduction with MapR
    • Storage
    • WriteAhead Log
    • Log Structured Merge Trees
  • HBase Basics
    • Schema Definition
    • Basic CRUD operations
  • HBase Clients
    • REST
    • Thrift
    • Avro
    • Web based UI



  • Introduction
  • How FLUME will be used – Flume agents


OOZIE during MapReduce

  • Introduction – Batch vs Real Time
  • How Oozie will be used – Scheduling jobs and Executing workflow jobs


SQOOP during Hive

  • Introduction
  • MySQL Client and Server Installation
  • Connecting to RDBMS using SQOOP
  • SQOOP Commands


Zookeeper during HBase

  • Introduction
  • Zookeeper Data Model
  • Zookeeper Services


Proof of Concept (POC)

  • Twitter Application – From Day1, start developing Twitter application by applying what we have learned in each phase
  • Real time use cases on each Topic


Highlights of the Training

  • Real time Project execution - hands on
  • Resume preparation and Job assistance
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