It is a prediction by the IT sectors that in the coming three to five years, around half of the data of the world will have to be processed on Hadoop. The demand for Hadoop experts is hence going to multiply into thousands and more.

adoop online training, thus, will introduce you to a big market of opportunities where your success and growth will be unlimited. The projection indeed point out irrespective of whether a particular enterprise is in need of a Big Data strategy or not, the requirement for Hadoop experts is to escalate in coming few years.

Through Hadoop online training, you can get Hadoop certification, which has been in huge demand in all types of industries including vertical services such as utilities, retail, financial services, pharmaceuticals and energy. There is a competition among the corporate to hire the best Hadoop professionals, which clearly demonstrate the hike in the demand for this field.


Hadoop online Training Course Content

Introduction to Big Data and Analytics
Introduction to Hadoop
Hadoop ecosystem – Concepts
Hadoop Map-reduce concepts and features
Developing the map-reduce Applications
Pig concepts
Hive concepts
Sqoop concepts
Flume Concepts
Oozie workflow concepts
Impala Concepts
Hue Concepts
HBASE Concepts
ZooKeeper Concepts
Real Life Use Cases

Reporting Tool


1. Virtualbox/VM Ware


2. Linux


3. Hadoop

Why Hadoop?
Distributed Framework
Hadoop v/s RDBMS
Brief history of hadoop

4. Setup hadoop

Pseudo mode
Cluster mode
Installation of java, hadoop
Configurations of hadoop
Hadoop Processes ( NN, SNN, JT, DN, TT)
Temporary directory
Common errors when running hadoop cluster, solutions

5. HDFS- Hadoop distributed File System

HDFS Design and Architecture
HDFS Concepts
Interacting HDFS using command line
Interacting HDFS using Java APIs

6. Hadoop Processes

Name node
Secondary name node
Job tracker
Task tracker
Data node

7. Map Reduce

Developing Map Reduce Application
Phases in Map Reduce Framework
Map Reduce Input and Output Formats
Advanced Concepts
Sample Applications

8. Joining datasets in Mapreduce jobs

Map-side join
Reduce-Side join

9. Map reduce customization

Custom Input format class
Hash Partitioner
Custom Partitioner
Sorting techniques
Custom Output format class

10. Hadoop Programming Languages :-


Installation and Configuration
Interacting HDFS using HIVE
Map Reduce Programs through HIVE
HIVE Commands
Loading, Filtering, Grouping
Data types, Operators
Joins, Groups
Sample programs in HIVE


Installation and Configurations


11. Introduction

12. The Motivation for Hadoop

Problems with traditional large-scale systems
Requirements for a new approach

13. 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

14. Writing a MapReduce Program

The MapReduce Flow
Examining a Sample MapReduce Program
Basic MapReduce API Concepts
The Driver Code
The Mapper
The Reducer
Hadoop’s Streaming API
Using Eclipse for Rapid Development
Hands-on exercise
The New MapReduce API

15. Common MapReduce Algorithms

Sorting and Searching
Machine Learning With Mahout
Term Frequency Inverse Document Frequency
Word Co-Occurrence
Hands-On Exercise.

16.PIG Concepts..

Data loading in PIG.
Data Extraction in PIG.
Data Transformation in PIG.
Hands on exercise on PIG.

17. Hive Concepts.

Hive Query Language.
Alter and Delete in Hive.
Partition in Hive.
Joins in Hive.Unions in hive.
Industry specific configuration of hive parameters.
Authentication & Authorization.
Statistics with Hive.
Archiving in Hive.
Hands-on exercise

18. Working with Sqoop

Import Data.
Export Data.
Sqoop Syntaxs.
Databases connection.
Hands-on exercise

19. Working with Flume

Configuration and Setup.
Flume Sink with example.
Flume Source with example.
Complex flume architecture.

20. OOZIE Concepts
21. IMPALA Concepts
22. HUE Concepts
23. HBASE Concepts
24. ZooKeeper concepts

Reporting Tool


Course Topics


What is visual analysis?
Strengths/weakness of the visual system.

Laying the Groundwork for Visual Analysis

Analytical Process
Preparing for analysis

Getting, Cleaning and Classifying Your Data

Cleaning, formatting and reshaping.
Using additional data to support your analysis.
Data classification

Visual Mapping Techniques

Visual Variables : Basic Units of Data Visualization
Working with Color
Marks in action: Common chart types

Solving Real-World Problems with Visual Analysis

Getting a Feel for the Data- Exploratory Analysis.
Making comparisons
Looking at (co-)Relationships.
Checking progress.
Spatial Relationships.
Try, try again.

Communicating Your Findings

Fine-tuning for more effective visualization
Storytelling and guided analytics

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