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Hadoop Training Courses

Courses / Hadoop Training Courses

Hadoop Training in Chennai

Hadoop is an open source distributed processing framework that manages data processing and storage for big data applications running in chaotic systems. 'ACTE' doesn’t stop with just providing the Hadoop Training at Anna nagar, Chennai , it also provides placement assistance and makes sure all our students get placed in top MNCs. It is at the center of a growing system of big data technologies that are primarily used to support advanced analytics initiatives, including predictive analytics, data mining and machine learning applications. Hadoop can handle various forms of structured and unstructured data, giving users more flexibility for collecting, processing and analyzing data than that provided by relational databases and data warehouses .

Hadoop Training in Chennai.

Hadoop is made up of four different modules, each of which carries out a particular task essential for a computer system designed for big data analytics. Our practised trainers of Hadoop Course at Anna nagar, Chennai can appendage you through the event of applications and analyses of massive knowledge. Which will prepare you for all the tasks that a work environment might put forth.

What’s the main target of Hadoop Course at Anna nagar, Chennai?

We are targeted in providing our candidates with practical knowledge rather than just technical expertise.

Who ought to take Best Hadoop Institute at Anna nagar, Chennai?

hadoop knowledge can change the creer course for the listed professional:

  • Software Developers and designers
  • Analytics Professionals
  • Senior IT professionals
  • Testing and Mainframe professionals
  • Data Management Professionals
  • Business Intelligence Professionals
  • Project Managers
  • Aspiring information Scientists
  • Graduates trying to make a career in massive information Analytics
  • Prerequisite for Best Hadoop Training Insitution at Anna nagar,Chennai:

    • Knowledge of Associate in Nursing package like UNIX operating system is helpful for the course.
    • For Spark we have a tendency to use Python Associate in Nursing Scale and an Ebook has been provided to assist you with constant.
    • As the data of Java is critical for this course, we have a tendency to area unit providing a complimentary access to “Java necessities for Hadoop” course.

Hadoop Training Course syllabus

  • This Course Covers 100% Developer and 40% Administration Syllabus.
  • For Hadoop Administrative,
  • Testing,
  • Analyst &
  • Custom Topic , Contact on 9383399991.

Introduction to BigData, Hadoop:-

  • Big Data Introduction
  • Hadoop Introduction
  • What is Hadoop? Why Hadoop?
  • Hadoop History?
  • Different types of Components in Hadoop?
  • HDFS, MapReduce, PIG, Hive, SQOOP, HBASE, OOZIE, Flume, Zookeeper and so on…
  • What is the scope of Hadoop?

Deep Drive in HDFS (for Storing the Data):-

  • Introduction of HDFS
  • HDFS Design
  • HDFS role in Hadoop
  • Features of HDFS
  • Daemons of Hadoop and its functionality
  • o Name Node
  • o Secondary Name Node
  • o Job Tracker
  • o Data Node
  • o Task Tracker
  • Anatomy of File Wright
  • Anatomy of File Read
  • Network Topology
  • o Nodes
  • o Racks
  • o Data Center
  • Parallel Copying using DistCp
  • Basic Configuration for HDFS
  • Data Organization
  • o Blocks and Replication
  • Rack Awareness
  • Heartbeat Signal
  • How to Store the Data into HDFS
  • How to Read the Data from HDFS
  • Accessing HDFS (Introduction of Basic UNIX commands)
  • CLI commands

MapReduce using Java (Processing the Data):-

  • The introduction of MapReduce.
  • MapReduce Architecture
  • Data flow in MapReduce
  • o Splits
  • o Mapper
  • o Portioning
  • o Sort and shuffle
  • o Combiner
  • o Reducer
  • Understand Difference Between Block and InputSplit
  • Role of RecordReader
  • Basic Configuration of MapReduce
  • MapReduce life cycle
  • o Driver Code
  • o Mapper
  • o and Reducer
  • How MapReduce Works
  • Writing and Executing the Basic MapReduce Program using Java
  • Submission & Initialization of MapReduce Job.
  • File Input/Output Formats in MapReduce Jobs
  • o Text Input Format
  • o Key Value Input Format
  • o Sequence File Input Format
  • o NLine Input Format
  • Joins
  • o Map-side Joins
  • o Reducer-side Joins
  • Word Count Example
  • Partition MapReduce Program
  • Side Data Distribution
  • o Distributed Cache (with Program)
  • Counters (with Program)
  • o Types of Counters
  • o Task Counters
  • o Job Counters
  • o User Defined Counters
  • o Propagation of Counters
  • Job Scheduling


  • Introduction to Apache PIG
  • Introduction to PIG Data Flow Engine
  • MapReduce vs. PIG in detail
  • When should PIG use?
  • Data Types in PIG
  • Basic PIG programming
  • Modes of Execution in PIG
  • o Local Mode and
  • o MapReduce Mode
  • Execution Mechanisms
  • o Grunt Shell
  • o Script
  • o Embedded
  • Operators/Transformations in PIG
  • PIG UDF’s with Program
  • Word Count Example in PIG
  • The difference between the MapReduce and PIG


  • Introduction to SQOOP
  • Use of SQOOP
  • Connect to mySql database
  • SQOOP commands
  • o Import
  • o Export
  • o Eval
  • o Codegen etc…
  • Joins in SQOOP
  • Export to MySQL
  • Export to HBase


  • Introduction to HIVE
  • HIVE Meta Store
  • HIVE Architecture
  • Tables in HIVE
  • o Managed Tables
  • o External Tables
  • Hive Data Types
  • o Primitive Types
  • o Complex Types
  • Partition
  • Joins in HIVE
  • HIVE UDF’s and UADF’s with Programs
  • Word Count Example


  • Introduction to HBASE
  • Basic Configurations of HBASE
  • Fundamentals of HBase
  • What is NoSQL?
  • HBase Data Model
  • o Table and Row
  • o Column Family and Column Qualifier
  • o Cell and its Versioning
  • Categories of NoSQL Data Bases
  • o Key-Value Database
  • o Document Database
  • o Column Family Database
  • HBASE Architecture
  • o HMaster
  • o Region Servers
  • o Regions
  • o MemStore
  • o Store
  • SQL vs. NOSQL
  • How HBASE is differed from RDBMS
  • HDFS vs. HBase
  • Client-side buffering or bulk uploads
  • HBase Designing Tables
  • HBase Operations
  • o Get
  • o Scan
  • o Put
  • o Delete


  • What is MongoDB?
  • Where to Use?
  • Configuration On Windows
  • Inserting the data into MongoDB?
  • Reading the MongoDB data.

Cluster Setup:--

  • Downloading and installing the Ubuntu12.x
  • Installing Java
  • Installing Hadoop
  • Creating Cluster
  • Increasing Decreasing the Cluster size
  • Monitoring the Cluster Health
  • Starting and Stopping the Nodes


  • Introduction Zookeeper
  • Data Modal
  • Operations


  • Introduction to OOZIE
  • Use of OOZIE
  • Where to use?


  • Introduction to Flume
  • Uses of Flume
  • Flume Architecture
  • o Flume Master
  • o Flume Collectors
  • o Flume Agents

    Project Explanation with Architecture

Course Highlights

  • Free demo classes.
  • Limited Batch Size.
  • Excellent lab facility.
  • Innovative ideas are taught by the professionals.
  • 100% placement Assurance.
  • Expert trainers will teach the students.
  • Certificates are provided to the students.

About Trainer

  • 4+ experienced in Hadoop
  • Certified trainer
  • Working in top MNC company
  • Friendly and interactive
  • Trained more than 3000 students
  • Ready to help students - 24/7
  • Strong knowledge and perfect delivery
  • On the spot doubt clarifying person

Praveen Raj
(Helical IT Solutions Pvt Limited)
4+ experience


  • Cloudera Certified Hadoop Developer (CCD-410)
  • HDP Certified Developer (HDPCD)
  • Our Reviews


    ACTE 5 Star Rating: Recommended 4.9 out of 5 based on 2094 ratings.