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Big Data and Hadoop Training In Hyderabad

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Course description

Rainbow Training Institute Offering Big Data Hadoop and Spark training course delivered by industry experts .Our trainers will covers in depth knowledge of  Big Data Hadoop and Spark with real time industry case study examples it will helps you master in Big Data Hadoop and Spark. This course will cover all Hadoop Ecosystem tolls such as Hive, Pig, HBase, Spark, Oozie, Flume and Sqoop,HDFS, YARN, MapReduce, Spark framework and RDD, Scala and Spark SQL, Machine Learning using Spark, Spark Streaming, etc.


Rainbow Training Institute Offering Big Data Hadoop and Spark online training and Data Hadoop and Spark class room Training.

Course Content

1 .Introduction to Big Data & Hadoop


ü  Importance of Data & Data Analysis

ü  What is Big Data?

ü  Big Data & its hype

ü  Big Data Users & Scenarios

ü  Structured vs Unstructured Data

ü  Challenges of Big Data

ü  How to overcome the challenges?

ü  Divide & Conquer philosophy

2 . Hadoop and its file system – HDFS


ü  History of Hadoop

ü  Hadoop Ecosystem

ü  Hadoop Animal Planet

ü  What is Hadoop?

ü  Key Distinctions of Hadoop

ü  Hadoop Components


ü  Map Reduce

ü  Why Distributed File System?

ü  The Design of HDFS

ü  Hadoop Distributed File System

ü  What is a HDFS block?

ü  Why HDFS block is so large in HDFS?

ü  Name Node

ü  Data Node

ü  Secondary NameNode

ü  A file in HDFS

ü  Hadoop Components/Architecture

ü  NameNode, JobTracker, DataNode, TaskTracker & Secondary Namenode

ü  Understanding Storage components(NameNode, DataNode & Secondary Namenode)

ü  Understanding Processing components(JobTracker & TaskTracker)

ü  How Secondary Namenode overcomes the failure of the primary Namenode

ü  Anatomy of a File Read

ü  Anatomy of a File Write


3.Understanding Hadoop Cluster


ü  Walkthrough of CDH VM setup

ü  Hadoop Cluster modes

ü  Standalone Mode

ü  Pseudo-Distributed Mode

ü  Distributed Mode

ü  Hadoop Configuration files

ü  core-site.xml

ü  mapred-site.xml

ü  hdfs-site.xml

ü  yarn-site.xml

ü  Understanding Cluster configuration




ü  Meet MapReduce

ü  WordCount algorithm – Traditional approach

ü  Traditional approach on a Distributed system& it’s drawbacks

ü  MapReduce approach

ü  Input & Output Forms of a MR program

ü  Hadoop Data types

ü  Map, Shuffle & Sort, Reduce Phases

ü  Workflow & Transformation of Data

ü  Word Count Code walkthrough

ü  Input Split & HDFS Block

ü  Relation between Split & Block

ü  MR Flow with Single Reduce Task

ü  MR flow with multiple Reducers

ü  Data locality Optimization

ü  Speculative Execution

ü  Combiner

ü  Partitioner


5.Advanced MapReduce


ü  Counters

ü  InputFormat & its hierarchy

ü  OutputFormat & its hierarchy

ü  Using Compression techniques

ü  Side Data Distribution – Distributed Cache

ü  Joins

ü  Map side join using Distributed Cache

ü  Reduce side Join

ü  Secondary Sorting

ü  MR Unit – An Unit testing framework




ü  What is Pig?

ü  Why Pig?

ü  Pig vs Sql

ü  Execution Types or Modes

ü  Running Pig

ü  Pig Data types

ü  Pig Latin relational Operators

ü  Multi Query execution

ü  Pig Latin Diagnostic Operators

ü  Pig Latin Macro & UDF statements

ü  Pig Latin Commands

ü  Pig Latin Expressions

ü  Schemas

ü  Pig Functions

ü  Pig Latin File Loaders

ü  Pig UDF & executing a Pig UDF

ü  Pig Use cases




ü  Introduction to Hive

ü  Pig vs. Hive

ü  Hive Limitations & Possibilities

ü  Hive Architecture

ü  Metastore

ü  Hive Data Organization

ü  Hive QL

ü  Sql vs. Hive QL

ü  Hive Data types

ü  Data Storage

ü  Managed & External Tables

ü  Partitions & Buckets

ü  Static Partitioning & Dynamic Partitioning

ü  Storage Formats

ü  File Formats – Sequence File & RC File

ü  Using Compression in Hive

ü  Built-in Serdes

ü  Importing Data (Using Load Data & Insert Into)

ü  Alter & Drop Commands

ü  Data Querying

ü  Using MR Scripts

ü  Hive Joins

ü  Sub Queries

ü  Views





ü  Introduction to NoSql & HBase

ü  HBase vs. RDBMS

ü  HBase Use cases

ü  Row & Column oriented storage

ü  Characteristics of a huge DB

ü  What is HBase?

ü  HBase Data-Model

ü  HBase logical model & physical storage

ü  HBase architecture

ü  HBase in operation (put, get, scan & delete)

ü  Loading Data into HBase

ü  HBase shell commands

ü  HBase operations through Java

ü  HBase operations through MR


9.ZooKeeper & Oozie


ü  Introduction to Zookeeper

ü  Distributed Coordination

ü  Zookeeper Data Model

ü  Zookeeper Service

ü  Introduction to Zookeeper

ü  Distributed Coordination

ü  Zookeeper Data Model

ü  Zookeeper Service




ü  Introduction to Sqoop

ü  Sqoop design

ü  Sqoop basic Commands

ü  Sqoop Table Import flow of execution

ü  Sqoop Import Commands – to HDFS, Hive & HBase tables

ü  Sqoop Incremental Import

ü  Incremental Append

ü  Incremental Last Modified

ü  Sqoop export flow of execution

ü  Sqoop Export Command




ü  Flume Architecture

ü  Flume Components

ü  Streaming live Twitter data with Flume


12.Hadoop 2.0 & YARN


ü  Hadoop 1 Limitations

ü  HDFS Federation

ü  NameNode High Availability

ü  Introduction to YARN

ü  YARN Applications

ü  YARN Architecture

ü  Anatomy of an YARN application




14.Spark Overview


ü  What is Spark?

ü  Why Spark?

ü  Spark & Big Data

ü  Spark Components

ü  Resilient Distributed Data sets

ü  Resilient Distributed Data sets

ü  Data Operations on RDD

ü  Spark Libraries

 Highlights of the Course:

Teaching is oriented towards

·         Practical oriented & Hands on

·         clear understanding of basics

·         what to expect as an interview question while topic discussion

ü  Exclusive Access to a variety of latest interview questions and answers

ü  Work on real-time projects(in all tools like – Pig, Hive, Mapreduce & HBase)

ü  Certification guidance & Material

ü  Handouts will be given which would serve as a knowledge-check

ü  Assistance in Resume preparation

ü  Interviews guidance

ü  Corporate level Training


Finally, this training gives you all that are needed to secure a desired job & keeps you get going in your job!


Course audience

ü  Software Developers, Project Managers

ü  Software Architects

ü  ETL and Data Warehousing Professionals

ü  Data Engineers

ü  Data Analysts & Business Intelligence Professionals

ü  DBAs and DB professionals

ü  Senior IT Professionals

ü  Testing professionals

ü  Mainframe professionals

ü  Software Developers, Project Managers

ü  Software Architects

ü  ETL and Data Warehousing Professionals

ü  Graduates looking to build a career in Big Data Field

Trainig Hours

Flexible Timing is available contact admin team.

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