Course Features

Course Details

About Hadoop Online Training:

Hadoop Training Course is an open-source framework that allows to store and process big data in a distributed environment across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.


This tutorial has been prepared for professionals aspiring to learn the basics of Big Data Analytics using Hadoop Framework and become a Developer. Software Professionals, Analytics Professionals, and ETL developers are the key beneficiaries of this course.


Before you start proceeding with the Hadoop Training in Hyderabad, we assume that you have prior exposure to Core Java, database concepts, and any of the Linux operating system flavors.

  Who Uses Hadoop?
A wide variety of companies and organizations use Hadoop for both research and production
What is Big Data?

Big Data Training is a collection of large datasets that cannot be processed using traditional computing techniques. It is not a single technique or a tool, rather it involves many areas of business and technology.

  What is Hadoop Training

Hadoop Course is an Apache open source framework written in Java that allows distributed processing of large datasets across clusters of computers using simple programming models. Its framework application works in an an environment that provides distributed storage and computation across clusters of computers. It is designed to scale up from a single server to thousands of machines, each offering local computation and storage.

  Hadoop Architecture:
Hadoop has two major layers namely:
(a)Processing/Computation layer (MapReduce), and
(b)Storage layer (Hadoop Distributed File System).
a) MapReduce

MapReduce is a parallel programming model for writing distributed applications devised at Google for efficient processing of large amounts of data (multi terabyte datasets), on large clusters (thousands of nodes) of commodity hardware in a reliable, fault-tolerant manner. The MapReduce program runs on     Hadoop which is an Apache open-source framework.

b) Hadoop Distributed File System 

The Hadoop Distributed File System (HDFS) is based on the Google File System (GFS) and provides a distributed file system that is designed to run on commodity hardware. It has many similarities with existing distributed file systems. However, the differences from other distributed file systems are significant. It is highly fault-tolerant and is designed to be deployed on low-cost hardware. It provides high throughput access to application data and is suitable for applications having large datasets.

  How Does Hadoop Work?
Hadoop runs code across a cluster of computers. This process includes the following core tasks that Hadoop performs:  
  • . Data is initially divided into directories and files. Files are divided into
uniformly sized blocks of 128M and 64M (preferably 128M).
  • . These files are then distributed across various cluster nodes for further processing.
  • . HDFS, being on top of the local file system, supervises the processing.
  • . Blocks are replicated for handling hardware failure.
  • . Checking that the code was executed successfully.
Advantages of Hadoop
Hadoop framework allows the user to quickly write and test distributed
systems. It is efficient, and it automatically distributes the data and work
on the machines and in turn, utilizes the underlying parallelism of the CPU cores.
    •  Hadoop does not rely on hardware to provide fault-tolerance and high availability (FTHA), rather it library itself has been designed to detect and handle failures at the application layer. Servers can be added or removed from the cluster dynamically and
    • Hadoop continues to operate without interruption.
    •  Another big advantage of Hadoop is that apart from being open source, it is compatible with all the platforms since it is Java based.

Hadoop/Bigdata Course Content

course Objective Summary
During this course, you will learn:
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
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
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..

This course is designed for the beginner to intermediate-level Tableau user. It is for anyone who works with data – regardless of technical or analytical background. This course is designed to help you understand the important concepts and techniques used in Tableau to move from simple to complex visualizations and learn how to combine them in interactive dashboards.

    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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    • The course is based totally live Project-Based Learning Approach.
    • Recorded periods of all the instructions can be supplied to you.
    • There can be Module-smart assignments and coding assignments to re-enforce your know- how.
    • After completion, of Course, certification and profession steerage will be furnished to every pupil.
    • instructor led Interactive on-line training and undertaking guidance
    • Assignments at quit of every magnificence to boost the standards.
    • We offer entire palms-on enjoy to each pupil on implementation of stay undertaking
    • 24x7 online help crew available to assist participants with any technical queries they'll have during the course.

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