Big Data & Hadoop Course

Rated Among the Top 10 Big Data & Hadoop Course


Big Data & Hadoop Training in Bangalore Offered by Prakalpana is the most powerful Big Data & Hadoop Training ever offered with Top Quality Trainers, Best Price, Certification, and 24/7 Customer Care. 


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 Get High-Quality Training, Certification, Best Price and 24/7 Customer Care.  

Success Factors:

  • High-Quality Training
  • Top 10+ years Technical Trainers
  • Comprehensive Course Curriculum
  • 100% Placement Assistance
  • Superb Satisfaction Score
  • Internship on Real-Time Project 

About Program

Prakalpna Technologies is a leading provider of Big Data & Hadoop Training in Bangalore. Prakalpana offers Big Data & Hadoop training which is huge in demand for Big Data & Hadoop professionals. Companies are looking for Big data & Hadoop experts with the knowledge of Hadoop Ecosystem and best practices about HDFS, MapReduce, Spark, HBase, Hive, Pig, Oozie, Sqoop & Flume. You can gain these skills with the Big Data Course. Get hands on exposure.

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    Curriculum Of Big Data & Hadoop Course

    • What is Big Data
    • Evolution of Big Data
    • Benefits of Big Data
    • Operational vs Analytical Big Data
    • Need for Big Data Analytics
    • Big Data Challenges
    • Master Nodes
      • Name Node
      • Secondary Name Node
      • Job Tracker
    • Client Nodes
    • Slaves
    • Hadoop configuration
    • Setting up a Hadoop cluster
    • Introduction to HDFS
    • HDFS Features
    • HDFS Architecture
    • Blocks
    • Goals of HDFS
    • The Name node & Data Node
    • Secondary Name node
    • The Job Tracker
    • The Process of a File Read
    • How does a File Write work?
    • Data Replication
    • Rack Awareness
    • HDFS Federation
    • Configuring HDFS
    • HDFS Web Interface
    • Fault tolerance
    • Name node failure management
    • Access HDFS from Java
    • Introduction to Yarn
    • Why Yarn
    • Classic MapReduce v/s Yarn
    • Advantages of Yarn
    • Yarn Architecture
      • Resource Manager
      • Node Manager
      • Application Master
    • Application submission in YARN
    • Node Manager containers
    • Resource Manager components
    • Yarn applications
    • Scheduling in Yarn
      • Fair Scheduler
      • Capacity Scheduler
    • Fault tolerance
    • What is MapReduce
    • Why MapReduce
    • How MapReduce works
    • Difference between Hadoop 1 & Hadoop 2
    • Identity mapper & reducer
    • Data flow in MapReduce
    • Input Splits
    • Relation Between Input Splits and HDFS Blocks
    • Flow of Job Submission in MapReduce
    • Job submission & Monitoring
    • MapReduce algorithms
      • Sorting
      • Searching
      • Indexing
      • TF-IDF
    • What is Hadoop
    • History of Hadoop
    • Hadoop Architecture
    • Hadoop Ecosystem Components
    • How does Hadoop work
    • Why Hadoop & Big Data
    • Hadoop Cluster introduction
    • Cluster Modes
      • Standalone
      • Pseudo-distributed
      • Fully – distributed
    • HDFS Overview
    • Introduction to MapReduce
    • Hadoop in demand
    • Starting HDFS
    • Listing files in HDFS
    • Writing a file into HDFS
    • Reading data from HDFS
    • Shutting down HDFS
    • Listing contents of directory
    • Displaying and printing disk usage
    • Moving files & directories
    • Copying files and directories
    • Displaying file contents
    • Object oriented concepts
    • Variables and Data types
    • Static data type
    • Primitive data types
    • Objects & Classes
    • Java Operators
    • Method and its types
    • Constructors
    • Conditional statements
    • Looping in Java
    • Access Modifiers
    • Inheritance
    • Polymorphism
    • Method overloading & overriding
    • Interfaces
      • Hadoop data types
      • The Mapper Class
        • Map method
      • The Reducer Class
        • Shuffle Phase
        • Sort Phase
        • Secondary Sort
        • Reduce Phase
      • The Job class
        • Job class constructor
      • JobContext interface
      • Combiner Class
        • How Combiner works
        • Record Reader
        • Map Phase
        • Combiner Phase
        • Reducer Phase
        • Record Writer
      • Partitioners
        • Input Data
        • Map Tasks
        • Partitioned Task
        • Reduce Task
        • Compilation & Execution

      Hadoop Ecosystems

      • 1.Pig:

        • What is Apache Pig?
        • Why Apache Pig?
        • Pig features
        • Where should Pig be used
        • Where not to use Pig
        • The Pig Architecture
        • Pig components
        • Pig v/s MapReduce
        • Pig v/s SQL
        • Pig v/s Hive
        • Pig Installation
        • Pig Execution Modes & Mechanisms
        • Grunt Shell Commands
        • Pig Latin – Data Model
        • Pig Latin Statements
        • Pig data types
        • Pig Latin operators
        • CaseSensitivity
        • Grouping & Co Grouping in Pig Latin
        • Sorting & Filtering
        • Joins in Pig latin
        • Built-in Function
        • Writing UDFs
        • Macros in Pig


        • What is HBase
        • History of HBase
        • The NoSQL Scenario
        • HBase & HDFS
        • Physical Storage
        • HBase v/s RDBMS
        • Features of HBase
        • HBase Data model
        • Master server
        • Region servers & Regions
        • HBase Shell
        • Create table and column family
        • The HBase Client API


        • Introduction to Apache Spark
        • Features of Spark
        • Spark built on Hadoop
        • Components of Spark
        • Resilient Distributed Datasets
        • Data Sharing using Spark RDD
        • Iterative Operations on Spark RDD
        • Interactive Operations on Spark RDD
        • Spark shell
        • RDD transformations
        • Actions
        • Programming with RDD
          • Start Shell
          • Create RDD
          • Execute Transformations
          • Caching Transformations
          • Applying Action
          • Checking output
        • GraphX overview


        • Introducing Cloudera Impala
        • Impala Benefits
        • Features of Impala
        • Relational databases vs Impala
        • How Impala works
        • Architecture of Impala
        • Components of the Impala
          • The Impala Daemon
          • The Impala Statestore
          • The Impala Catalog Service
        • Query Processing Interfaces
        • Impala Shell Command Reference
        • Impala Data Types
        • Creating & deleting databases and tables
        • Inserting & overwriting table data
        • Record Fetching and ordering
        • Grouping records
        • Using the Union clause
        • Working of Impala with Hive
        • Impala v/s Hive v/s HBase

        5.MongoDB Overview:

        • Introduction to MongoDB
        • MongoDB v/s RDBMS
        • Why & Where to use MongoDB
        • Databases & Collections
        • Inserting & querying documents
        • Schema Design
        • CRUD Operations

        6.Oozie & Hue Overview:

        • Introduction to Apache Oozie
        • Oozie Workflow
        • Oozie Coordinators
        • Property File
        • Oozie Bundle system
        • CLI and extensions
        • Overview of Hue


        • What is Hive?
        • Features of Hive
        • The Hive Architecture
        • Components of Hive
        • Installation & configuration
        • Primitive types
        • Complex types
        • Built in functions
        • Hive UDFs
        • Views & Indexes
        • Hive Data Models
        • Hive vs Pig
        • Co-groups
        • Importing data
        • Hive DDL statements
        • Hive Query Language
        • Data types & Operators
        • Type conversions
        • Joins
        • Sorting & controlling data flow
        • local vs mapreduce mode
        • Partitions
        • Buckets


        • Introducing Sqoop
        • Scoop installation
        • Working of Sqoop
        • Understanding connectors
        • Importing data from MySQL to Hadoop HDFS
        • Selective imports
        • Importing data to Hive
        • Importing to Hbase
        • Exporting data to MySQL from Hadoop
        • Controlling import process


        • What is Flume?
        • Applications of Flume
        • Advantages of Flume
        • Flume architecture
        • Data flow in Flume
        • Flume features
        • Flume Event
        • Flume Agent
          • Sources
          • Channels
          • Sinks
        • Log Data in Flume

        10.Zookeeper Overview:

        • Zookeeper Introduction
        • Distributed Application
        • Benefits of Distributed Applications
        • Why use Zookeeper
        • Zookeeper Architecture
        • Hierarchial Namespace
        • Znodes
        • Stat structure of a Znode
        • Electing a leader

        11.Kafka Basics:

        • Messaging Systems
          • Point-to-Point
          • Publish – Subscribe
        • What is Kafka
        • Kafka Benefits
        • Kafka Topics & Logs
        • Partitions in Kafka
        • Brokers
        • Producers & Consumers
        • What are Followers
        • Kafka Cluster Architecture
        • Kafka as a Pub-Sub Messaging
        • Kafka as a Queue Messaging
        • Role of Zookeeper
        • Basic Kafka Operations
          • Creating a Kafka Topic
          • Listing out topics
          • Starting Producer
          • Starting Consumer
          • Modifying a Topic
          • Deleting a Topic
        • Integration With Spark

        12.Scala Basics:

        • Introduction to Scala
        • Spark & Scala interdependence
        • Objects & Classes
        • Class definition in Scala
        • Creating Objects
        • Scala Traits
        • Basic Data Types
        • Operators in Scala
        • Control structures
        • Fields in Scala
        • Functions in Scala
        • Collections in Scala
          • Mutable collection
          • Immutable collection


    Priyanka HS
    Priyanka HS
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    I've been here for SpringBoot & Microservices course. Tutors are professional with in-depth knowledge, using simple examples and making it easy to understand. Course work is scheduled in such a way it includes much of assignments. I got zero knowledge on programming when i started, But now I'm able to code. I would recommend it to anyone.
    Vikash Kumar
    Vikash Kumar
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    I have been Learning Docker & Kubernetes course. My trainer taught me very in-depth hands on using simple examples and making it easy to understand. It help me to grab job in very good MNC.
    Habeeba Taj
    Habeeba Taj
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    Thank u so much for your very valuable training and Prakalpana support team also helped me lot answering all of my question the instructor also very excellent.


    Big Data Hadoop Course is one of the accelerating and most promising fields, considering all the technologies available in the IT market today. In order to take advantage of these opportunities, you need a structured The Big Data Hadoop course is designed to provide knowledge and skills to become a successful Big Data Hadoop . At the end of the course the participants will have an understanding of all the basic and advanced concepts like Request flow, Rendering systems, Big Data Hadoop Training Course with the latest curriculum as per current industry requirements and best practices.

    Besides a strong theoretical understanding, you need to work on various real-world Procurement & Logistics projects using different industrial Big Data Hadoop Training as a part of solution strategy.

    Additionally, you need the guidance of a Big Data Hadoop  Training expert who is currently working in the industry on real-world Big Data Hadoop  Training projects and troubleshooting day-to-day challenges while implementing them. All of which can be acquired from the Big Data Hadoop Training Course.

    Prakalpana Technologies provides many suitable modes of training .

    • Classroom training 
    • Online training 
    • Fast track & Super fast track
    • Live instructor online training
    • Customized training

    We do, however, provide recordings of each session you attend for your future reference.

    Yes. We arrange a free demo for all the courses either in the Classroom or Live-Online demo. Please fill the Schedule demo  form to schedule a free demo.

    All our trainers are certified and are highly qualified, with multiple years of experience in working with front-end development technology.

    You will receive Prakalpana Technologies globally recognized course completion certificate

    Yes you will get placement assistance after the course.

    Give  us a quick CALL to our course advisor  at +917505363802 / +919945619267 OR email at [email protected]

    You can reach to the support team for your queries 24/7 .  The team will help you in resolving queries, during and after the course. 

    You can reach to the Corporate Training team at 24/7 on this +917505363802 / +919945619267 OR email at [email protected]

    Training Features

    Classroom Training

    Prakalpana offers Classroom training for all courses in Bangalore and top courses as a scheduled batch in Prakalpana.

    Live-Online Training

    Prakalpana offers Live - Online training for all courses in Bangalore and top courses as a scheduled batch in Prakalpana.

    Real-life Case Studies

    Prakalpana offers Real - Lifecase study training in all courses in Bangalore with our top IT 10+ Years Instructures.


    Prakalpana have a community forum for our learners that further facilitates learning through peer interaction and knowledge sharing.

    24 x 7 Expert Support

    Prakalpana have a lifetime 24x7 online support team to resolve all your technical queries in a short time.


    After sucessfully completing your final course , Prakalpana will certify you as an Big Data & Hadoop Engineer.