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Hadoop Training

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Hadoop Course Overview

Apache Hadoop is an open source platform that is used to efficiently store and process massive datasets ranging in size from gigabytes to petabytes. Instead of using a single computer to store and process data, Hadoop allows many computer clusters to analyse huge datasets more rapidly in parallel.

HKR delivers the best industry-oriented hadoop training course that is in line to clear the certification exams. Our course covers all the key concepts such as key fundamentals of hadoop, hadoop concepts, building blocks of hadoop, apache hive, flume, streaming, big data hadoop optimizations, etc. During the training period, you can get full support and real-time project assistance from experienced professionals. Enroll today at HKR for accepting the new challenges to make the best out of our hadoop online training.


To apply for the Hadoop Training, you need to either:

  • To learn big data Analytics tools you need to know at least one programming language like Java, Python or R.
  • You must also have basic knowledge on databases like SQL to retrieve and manipulate data.
  • You need to have knowledge on basic statistics like progression, distribution, etc. and mathematical skills like linear algebra and calculus.

Hadoop Course Content

The Hadoop course curriculum is structured to streamline the learning process by a team of experts. You can find the complete course details in below-mentioned modules

1.1 Introduction to Big Data and Hadoop

1.2 Introduction to Big Data

1.3 Big Data Analytics

1.4 What is Big Data

1.5 Four Vs Of Big Data

1.6 Case Study Royal Bank of Scotland

1.7 Challenges of Traditional System

1.8 Distributed Systems

1.9 Introduction to Hadoop

1.10 Components of Hadoop Ecosystem 

1.11 Commercial Hadoop Distributions

2.1 Introduction to Hadoop Architecture Distributed Storage (HDFS) and YARN

2.2 What Is HDFS

2.3 Need for HDFS

2.4 Regular File System vs HDFS

2.5 Characteristics of HDFS

2.6 HDFS Architecture and Components

2.7 High Availability Cluster Implementations

2.8 HDFS Component File System Namespace

2.9 Data Block Split

2.10 Data Replication Topology

2.11 HDFS Command Line

2.12 YARN Introduction

2.13 YARN Use Case

2.14 YARN and Its Architecture

2.15 Resource Manager

2.16 How Resource Manager Operates

2.17 Application Master

2.18 How YARN Runs an Application

2.19 Tools for YARN Developers

3.1 Introduction to Data Ingestion into Big Data Systems and ETL

3.2 Overview of Data Ingestion

3.3 Apache Sqoop

3.4 Sqoop and Its Uses

3.5 Sqoop Processing

3.6 Sqoop Import Process

3.7 Sqoop Connectors

3.8 Apache Flume

3.9 Flume Model

3.10 Scalability in Flume

3.11 Components in Flume’s Architecture

3.12 Configuring Flume Components

3.13 Apache Kafka

3.14 Aggregating User Activity Using Kafka

3.15 Kafka Data Model

3.16 Partitions

3.17 Apache Kafka Architecture

3.18 Producer Side API Example

3.19 Consumer Side API

3.20 Consumer Side API Example

3.21 Kafka Connect

4.1 Introduction to Distributed Processing MapReduce Framework and Pig

4.2 Distributed Processing in MapReduce

4.3 Word Count Example

4.4 Map Execution Phases

4.5 Map Execution Distributed Two Node Environment

4.6 MapReduce Jobs

4.7 Hadoop MapReduce Job Work Interaction

4.8 Setting Up the Environment for MapReduce Development

4.9 Set of Classes

4.10 Creating a New Project

4.11 Advanced MapReduce

4.12 Data Types in Hadoop

4.13 OutputFormats in MapReduce

4.14 Using Distributed Cache

4.15 Joins in MapReduce

4.16 Replicated Join

4.17 Introduction to Pig

4.18 Components of Pig

4.19 Pig Data Model

4.20 Pig Interactive Modes

4.21 Pig Operations

4.22 Various Relations Performed by Developers

5.1 Introduction to Apache Hive

5.2 Hive SQL over Hadoop MapReduce

5.3 Hive Architecture

5.4 Interfaces to Run Hive Queries

5.5 Running Beeline from Command Line

5.6 Hive Metastore

5.7 Hive DDL and DML

5.8 Creating New Table

5.9 Data Types

5.10 Validation of Data

5.11 File Format Types

5.12 Data Serialization

5.13 Hive Table and Avro Schema

5.14 Hive Optimization Partitioning Bucketing and Sampling

5.15 Non-Partitioned Table

5.16 Data Insertion

5.17 Dynamic Partitioning in Hive

5.18 Bucketing

5.19 What Do Buckets Do

5.20 Hive Analytics UDF and UDAF

5.21 Other Functions of Hive

6.1 Introduction to NoSQL Databases HBase

6.2 NoSQL Introduction

6.3 HBase Overview

6.4 HBase Architecture

6.5 Data Model

6.6 Connecting to HBase

7.1 Introduction to the basics of Functional Programming and Scala

7.2 Introduction to Scala

7.3 Functional Programming

7.4 Programming with Scala

7.5 Type Inference Classes Objects and Functions in Scala

7.6 Collections

7.7 Types of Collections

7.8 Scala REPL

8.1 Introduction to Apache Spark Next-Generation Big Data Framework

8.2 History of Spark

8.3 Limitations of MapReduce in Hadoop

8.4 Introduction to Apache Spark

8.5 Components of Spark

8.6 Application of In-Memory Processing

8.7 Hadoop Ecosystem vs Spark

8.8 Advantages of Spark

8.9 Spark Architecture

8.10 Spark Cluster in Real World

9.1 Processing RDD

9.2 Introduction to Spark RDD

9.3 RDD in Spark

9.4 Creating Spark RDD

9.5 Pair RDD

9.6 RDD Operations

9.7 Demo: Spark Transformation Detailed Exploration Using Scala Examples

9.8 Demo: Spark Action Detailed Exploration Using Scala

9.9 Caching and Persistence

9.10 Storage Levels

9.11 Lineage and DAG

9.12 Need for DAG

9.13 Debugging in Spark

9.14 Partitioning in Spark

9.15 Scheduling in Spark

9.16 Shuffling in Spark

9.17 Sort Shuffle

9.18 Aggregating Data with Pair RDD

10.1 Introduction to Spark SQL Processing DataFrames

10.2 Spark SQL Introduction

10.3 Spark SQL Architecture

10.4 DataFrames

10.5 Demo: Handling Various Data Formats

10.6 Demo: Implement Various DataFrame Operations

10.7 Demo: UDF and UDAF

10.8 Interoperating with RDDs

10.9 Demo: Process DataFrame Using SQL Query

10.10 RDD vs DataFrame vs Dataset

11.1 Introduction to Spark MLlib Modeling Big Data with Spark

11.2 Role of Data Scientist and Data Analyst in Big Data

11.3 Analytics in Spark

11.4 Machine Learning

11.5 Supervised Learning

11.6 Demo: Classification of Linear SVM

11.7 Demo: Linear Regression with Real-World Case Studies

11.8 Unsupervised Learning

11.9 Demo: Unsupervised Clustering K-Means

11.10 Reinforcement Learning

11.11 Semi-Supervised Learning

11.12 Overview of MLlib

11.13 MLlib Pipelines

12.1 Introduction to Stream Processing Frameworks and Spark Streaming

12.2 Overview of Streaming 

12.3 Real-Time Processing of Big Data

12.4 Data Processing Architectures

12.5 Spark Streaming

12.6 Introduction to DStreams

12.7 Transformations on DStreams

12.8 Design Patterns for Using ForeachRDD

12.9 State Operations

12.10 Windowing Operations

12.11 Join Operations stream-dataset Join

12.12 Streaming Sources

12.13 Structured Spark Streaming

12.14 Use Case Banking Transactions

12.15 Structured Streaming Architecture Model and Its Components

12.16 Output Sinks

12.17 Structured Streaming APIs

12.18 Constructing Columns in Structured Streaming

12.19 Windowed Operations on Event-Time

12.20 Use Cases

13.1 Introduction to Spark GraphX

13.2 Introduction to Graph

13.3 Graphx in Spark

13.4 Graph Operators

13.5 Join Operators

13.6 Graph Parallel System

13.7 Algorithms in Spark

13.8 Pregel API

13.9 Use Case of GraphX

Hadoop Projects

Project 1

Analyzing Historical Insurance Claims

Use Hadoop features to predict patterns and share actionable insights for a car insurance company.

Project 2

Analyzing Intraday Price Changes

Use Hive features for data engineering and analysis of New York stock exchange data.

Project 3

Analyzing Employee Sentiment

Perform sentiment analysis on employee review data gathered from Google, Netflix, and Facebook.

Project 4

Analyzing Product Performance

Perform product and customer segmentation to increase the sales of Amazon.

Hadoop Training Options


  • Interactive sessions
  • Learn by doing
  • Instant doubt resolution
  • Expert's Guidance
  • Industry-ready skills
Batch Start Date Time
Fast Track 21-Jun - 11-Jul 09:30 AM IST
Weekday 25-Jun - 25-Jul 11:30 AM IST
Weekend 29-Jun - 29-Jul 01:30 PM IST


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I had an insightful experience with HKR Trainings while participating in the ServiceNow ITOM (IT Operations Management) Training online. Engaging in instructor-led sessions, the trainer offered detailed insights into various ServiceNow ITOM modules and practices. Throughout the course, the support team was consistently available, and the trainer adeptly clarified all my inquiries, ensuring a comprehensive understanding of ServiceNow ITOM concepts.
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I had a comprehensive learning journey with HKR Trainings while undertaking the PostgreSQL Training online. Engaging in instructor-led sessions, the trainer delved deep into various PostgreSQL functionalities and best practices. Throughout the training, the support team remained attentive, and the trainer skillfully addressed all my questions, facilitating a solid grasp of PostgreSQL concepts.
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I had a rewarding experience with HKR Trainings while delving into the ServiceNow ITOM (IT Operations Management) Training online. Engaging in instructor-led sessions, the trainer provided comprehensive insights into various ServiceNow ITOM modules and best practices. Throughout the course, the support team was consistently available, and the trainer adeptly addressed all my queries, ensuring a robust understanding of ServiceNow ITOM concepts.

Hadoop Online Training Objectives

The Hadoop training course benefits for the following list of professionals.

  • Programming Developers and System Administrators.
  • Experienced working professionals and Project Managers.
  • Big Data Hadoop Developers eager to learn other verticals like testing, analytics and administration.
  • Mainframe Professionals, Architects and Testing Professionals.
  • Business Intelligence, Data Warehousing and Analytics Professionals.
  • Graduates and undergraduates eager to learn Big Data.

Aspirants should have a basic understanding of Core Java and SQL. 

To start with the Hadoop training course, you need to check with the best institute that delivers knowledge. Before proceeding to join any training, take suggestions from the experts who had already learned the course. We at HKR, with a team of industry experts, are ready to fulfil your dream career to achieve a job in desired companies.

Once you complete the entire course along with real-time projects and assignments, HKR delivers the course completion certification. This certification helps to get a job in any company very quickly.

Our trainers are highly qualified and certified with many years of industry experience with a technical background in Hadoop.

Certification differentiates you from the non-certified peers, and you can demand the best salary in the leading companies.

We, at HKR, provide complete guidance to reach your dream job. But your job will be based on your performance in the interview panel and the recruiter requirements.


Each and every class is recorded so if you missed any class you can review the recordings and clarify any doubts with the trainer in next class.   

Yes, we don’t assure 100% placement assistance. We are tied up with some corporate companies so when they have a requirement we send your profiles to them.

Yes, we provide demo before starting any training in which you can clear all your doubts before starting training.

Our trainers are real-time experts who are presently working on a particular platform on which they are providing training.

You can call our customer care 24/7

Max of the students gets satisfied with our training if you are not then we provide specialised training in return.

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