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Hadoop Training in India

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Hadoop Training India - Course Overview

Apache Hadoop is an accumulation of open-source software utilities that make it easier to solve problems involving massive amounts of data and computing using a network of multiple computers. It offers an integrated framework for data storage and processing of large data using the MapReduce programming model.Hadoop was designed specifically for multiple computers built from commodity hardware, which would still be commonly used. Since then, use has also been found on higher-end hardware clusters. All components in Hadoop are engineered with the key principle that hardware failures are regular events and should be managed immediately by the framework.

HKR trainings is one of the most successful e-learning platforms that delivers comprehensive best hadoop training in India. This training benefits both the newbies and experienced learners as well. During the  training you will gain hands on experience with the four important components of hadoop namely developer, administrator, testing and analyst. By the endo fht etraining you will be more proficient and can easily attain the cloudera hadoop certification. During the training period, you will experience good support and real time project assistance. Get enrolled to make the best out of your hadoop certification training course in India.


To apply for the Hadoop Training in India, 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 Training India - Course Content

HKR trainings delivers the most curated hadoop course content in India with a team of qualified and professional experts in hadoop technology. One can have  quick access to the optimized hadoop course structure in handy.

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 Online Training in India - Projects


Analyzing Historical Insurance Claims

By taking advantage of the  Hadoop features one needs to predict patterns and start sharing valuable insights with a car insu.....rance company. Read more


Analyzing Intraday Price Changes

By taking advantage of the Hive features for data engineering and stock exchange data analysis in New York. 


Analyzing Employee Sentiment

The project deals with performing the sentiment analysis of employee data collected from Google, Netflix, and Facebook. ..... Read more


Analyzing The Product Performance

In this project we need to perform product and customer segmentation in order to increase Amazon's sales that benefits the organiz.....ation growth. Read more

Hadoop Training India Options


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  • Instant doubt resolution
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  • Industry-ready skills
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Weekend 2-Jun - 2-Jul 11:30 AM IST
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Our Learners

Harshad Gaikwad

Harshad Gaikwad

Practice Head

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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Balaji Gnanasekar

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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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Amit Singh

Technical Lead - Service Now

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 Training in India Objectives

The Hadoop Training Certification offered in India was designed for professionals with and without work experience in any of the profiles below: 

  • Freshers / Officials who are very much excited to learn big data 
  • Who wants to seek their career as a hadoop administrator
  • Project managers,
  • Hadoop developers,
  • Data warehousing and Analytical professionals,
  • System administrators,

Well, there is no special requirement to take up the Hadoop training in India. The aspirants who wish to learn or build their career in data analytics can take up this course.

To commence your hadoop training course in Hyderabad, people therefore need to check with the perfect institute which really provides information. Before moving ahead with any training, accept advice from the professionals that have already experienced the course. We at HKR with a squad of industry experts, are ready to fulfil your future career in order to get a job in the company you want.

Immediately after the successful completion of the course which is accompanied by the real time projects task and assignments, HKR trainings in India offers you the course completion certificate. This certification differentiates you from the other non-certified peers and also helps to get a job in any company in India very quickly.

All our HKR trainers in India are well experienced and expert professionals with many years of relevant industry, working experience. Moreover the trainers at HKR trainings had a special attraction because each and every trainer is certified or highly specialised in their preferred technology.

Hadoop certification training in India differentiates you from the non-certified peers, and you can demand the best salary in the leading companies in India.

Definitely the experts at HKR trainings provides you with excellent job assistance facilities but not the job. They will guide , support and assist to enhance your future growth. However, your employment will be based on your skills exposed in the interview process 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 particular platform on which they are providing training.

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

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