Hadoop Training in Chennai:

Besant technologies provide you best Hadoop Training in Chennai from expert trainers in industry, our Hadoop training approaches meets the current corporate needs and requirements. All knows Hadoop is undoubtedly most popular and emerging technology to learn.

Why Hadoop and what you learn in Besant technologies?

Hadoop is booming technology in now a day’s digital world. We are expecting to handle more data’s and huge storage required in distributed computing environment. Everything is now on cloud, it is been challenging task for developer to handle extremely huge data. To overcome this, Hadoop has been introduced by Apache and becoming most popular technology in industry as soon. Reason, Hadoop is widely used open source, supports processing and storage of huge data in cloud environment.

In Besant technologies, we provide high standard training of Hadoop and we are the Best Hadoop Training Institute in Chennai. We have carefully crafted our Hadoop Training Syllabus to suite both newbie and experienced person.

To whom Hadoop Training is suitable?

Our Hadoop training syllabus is suitable for both beginners and experienced professionals. So anyone can learn Hadoop. Very little coding knowledge and management skill is required to learn Hadoop. Also it perfectly suited for person who is looking for career in data analyst, data scientist.

Job Opportunities in Hadoop:

There are many companies looking for Hadoop professional to provide successful and comprehensive big data service to their clients.  We at besant technologies, also provide Hadoop Job assistance for our students and we hold the pride of placing more than 250+ students in various MNC’S.

Hadoop Certification and Hadoop Job Assistance Service

We have customized syllabus for both fresher’s and experienced person. We will provide assistance to our trainees for Cloudera Developer Certification. All our topics provide you in-depth knowledge in Hadoop Big Data. Step into our Hadoop training in Veclahery, OMR and Tambaram branches

Hadoop Training Syllabus



  • Basic Understanding of Linux commands
  • Basic Understanding of Java & Scala
  • Good knowledge on DB

Big Data Opportunities & Challenges:

  • Introduction to 3V
  • BigData & Hadoop
  • OOPS & Java Fundamentals

Understanding Linux Commands:

  • Linux commands required for Hadoop

Introduction to Hadoop:

  • Concept of Hadoop Distributed file system(HDFS)
  • Design of HDFS
  • Common challenges
  • Best practices for scaling with your data
  • Configuring HDFS
  • Interacting with HDFS
  • HDFS permission and Security
  • Additional HDFS Tasks
  • Data Flow – Anatomy of a File Read, Anatomy of a File Write
  • Hadoop Archives

Getting Started with Hadoop:

  • Creating & Running your program

Pseudo Cluster Environment – Setting up Hadoop Cluster:

  • Cluster specification
  • Hadoop Configuration (Environment Settings, Hadoop Daemon- Properties, Addresses and
  • Basic Linux and HDFS Commands
  • Setup a Hadoop Cluster


  • Hadoop Data Types
  • Functional-Concept of Mappers
  • Functional-Concept of Reducers
  • The Execution Framework
  • Concept of Partioners
  • Functional- Concept of Combiners
  • Hadoop Cluster Architecture
  • MapReduce types
  • Input Formats (Input Splits and Records, Text Input, Binary Input, Multiple Inputs)
  • OutPut Formats (TextOutput, BinaryOutPut, Multiple Output).
  • Writing Programs for MapReduce


  • Installing and Running Pig
  • Grunt
  • Pig’s Data Model
  • Pig Latin
  • Developing & Testing Pig Latin Scripts
  • Writing Evaluation
  • Filter
  • Loads & Store Functions


  • Hive Architecture
  • Running Hive
  • Comparison with Traditional Database (Schema on Read versus Write, Updates, Transactionsand Indexes)
  • HiveQL (Data Types, Operators and Functions)
  • Tables (Managed and External Tables, Partitions and Buckets, Storage Formats, Importing Data)
  • Altering Tables, Dropping Tables
  • Querying Data (Sorting And Aggregating, Map Reduce Scripts, Joins & Subqueries & Views
  • Map and Reduce site Join to optimize Query
  • User Defined Functions
  • Appending Data into existing Hive Table
  • Custom Map/Reduce in Hive
  • Perform Data Analytics using Pig and Hive


  • Introduction
  • Client API- Basics
  • Client API- Advanced Features
  • Client API – Administrative Features
  • Available Client
  • Architecture
  • MapReduce Integration
  • Advanced Usage
  • Advanced Indexing
  • Implement HBASE


  • Database Imports
  • Working with Imported data
  • Importing Large Objects
  • Performing Exports
  • Exports- A Deeper look


  • The Zookeeper Service (Data Modal, Operations, Implementation, Consistency, Sessions, States)


  • Workflow
  • Coordinator
  • Flume
  • Concepts and Real time data streaming
  • Introduction to Kafka
  • Projects related to MapReduce, Pig, Hive and Sqoop & Flume

Spark Introduction:

  • What is Spark? Why Spark?
  • Spark Ecosystem
  • Overview of Scala
  • Why Scala?
  • Mapreduce Vs Spark

Hello Spark:

  • my First Program in Spark
  • Overview of RDD

Spark Installation:

  • Installing Spark on Standalone cluster

RDD Fundamentals:

  • Purpose and structure of RDD’s
  • Transformations
  • Actions
  • Programming API

Spark SQL/DataFrames:

  • Dataframes / SQL APIs
  • Uses of it

Strong>Spark Streaming:

  • Sources and Tasks
  • Dstream APIs
  • Reliability and Fault Recovery
  • Projects related to Spark SQL will be assigned