BigData Hadoop Certification Training Course in Pune

Bigdata Hadoop Ecosystem Tools and Libraries
Duration of course: 50 hrs

Best Blended Syllabus for Bigdata Hadoop Training in Pune by a 100% Placement-Oriented Training Institute

Hadoop training and certification course provide in-depth knowledge of Hadoop Ecosystem tools and Big Data. Here you learn Python & Spark, Sqoop, HDFS, MapReduce, Hive, HBase, Oozie, ZooKeeper, Pig, Flume, YARN by working on Big Data Hadoop Capstone Project (Implementing Big Data Lake for Heterogeneous Data Sources). Big data Hadoop course is designed by industry experts with in-depth knowledge of Hadoop Ecosystem tools and Big Data.

Instructor-led BigData Hadoop Live Online Interactive Training

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26th Jun
26th Jun

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BigData Hadoop Course​

Best Blended Syllabus for BigData Hadoop Training in Pune by a 100% Placement-Oriented Training Institute

  • Hadoop- Demo
  • What is Bigdata
  • When data becomes Bigdata
  • 3V’s of Bigdata
  • Introduction to Hadoop Ecosystem
  • Why Hadoop? If Existing Tools and Technologies are there in the market for decades?
  • How Hadoop is getting two categories Projects- New projects on Hadoop
  • Clients want POC and migration of Existing tools and Technologies on Hadoop Technology
  • How Open Source tool (HADOOP) is capable to run jobs in lesser time which take longer time in other tools in the market.
  • Hadoop Processing Framework (Map Reduce) / YARN
  • Alternates of Map Reduce
  • Why NoSQL is in more demand nowadays
  • Distributed warehouse for DFS
  • Most demanding tools which can run on the top of Hadoop Ecosystem for specific requirements in specific scenarios
  • Data import/Export tools

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  • Hadoop installation
  • Introduction to Hadoop FS and Processing Environment’s UIs
  • How to read and write files
  • Basic Unix commands for Hadoop
  • Hadoop’s FS shell
  • Hadoop’s releases
  • Hadoop’s daemons

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  • Hive Introduction
  • Hive Advanced
  • Partitioning
  • Bucketing
  • External Tables
  • Complex Use cases in Hive
  • Hive Advanced Assignment
  • Real-time scenarios of Hive

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  • How Map Reduce works as Processing Framework
  • End to End execution flow of Map Reduce job
  • Different tasks in Map Reduce job
  • Why Reducer is optional while Mapper is mandatory?
  • Introduction to Combiner
  • Introduction to Partitioner
  • Programming languages for Map Reduce
  • Why Java is preferred for Map Reduce programming
  • POC based on Pig, Hive, HDFS, MR

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  • How to work on Map Reduce in real-time
  • Map Reduce complex scenarios
  • Drawbacks of Hadoop
  • Why Hadoop can’t be used for real-time processing

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  • Introduction to Zookeeper
  • How Zookeeper helps in Hadoop Ecosystem
  • How to load data from Relational storage in Hadoop
  • Sqoop basics Sqoop practical implementation
  • Quick revision of previous classes to fill the gap in understanding and correct understandings

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  • How to load data in Hadoop that is coming from the web server or other storage without fixed schema
  • How to load unstructured and semi-structured data in Hadoop
  • Introduction to Flume
  • Hands-on on Flume
  • How to load Twitter data in HDFS using Hadoop
  • Introduction to Oozie
  • What kind of jobs can be scheduled using Oozie
  • How to schedule time-based jobs
  • Hadoop releases
  • From where to get Hadoop and other components to install
  • Introduction to YARN
  • Significance of YARN

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  • Introduction to Hue
  • How Hue is used in real-time
  • Real-time Hadoop usage
  • Real-time cluster introduction
  • Hadoop Release 1 vs Hadoop Release 2 in real-time
  • Hadoop real-time project
  • Major POC based on the combination of several tools of Hadoop Ecosystem
  • Datasets for practice purpose

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  • Introduction to Spark
  • Introduction to Python
  • PySpark concepts
  • Advantages of Spark over Hadoop
  • Is Spark a replacement for Hadoop?
  • How Spark is Faster than Hadoop
  • Spark RDD
  • Spark Transformation and Actions
  • Spark SQL
  • Datasets and Data Frames
  • Real-time scenarios examples of Spark where we prefer Spark over Hadoop
  • How Spark is capable to process complex data sets in lesser time
  • In-Memory Processing Framework for Analytics

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  • Introduction to Cloud Computing
  • On-premises vs cloud setup
  • Major cloud providers of Bigdata
  • What is EMR
  • HDFS vs S3
  • Overview and working of AWS Glue jobs
  • AWS Glue
  • AWS Redshift
  • AWS Athena

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Bigdata Hadoop Training Completion Certificate​

To obtain the Big Data-Hadoop Certification, you have to fulfil the following criteria :

  • Complete the Big Data hadoop course online/classroom course syllabus.
  • You can attend multiple batches of the same trainer & complete the Big Data hadoop course training.
  • Completion of all assignments & capstone project.

Batches Completed

Industry Oriented Syllabus

Designed By Expert


Happy Students

Self Assessments

Quizzes, POC


8+ Years Of Experience

Recorded Sessions

1 Year Of Access

Bigdata Hadoop Training Completion Certificate


Course Benefits​

  • Pay only after Attending one FREE TRIAL OF RECORDED LESSON.
  • Prerequisite – Basic SQL
  • Course designed for non-IT as well as IT professionals.
  • Flexible batch switch is available.
  • Classroom & Online Training – Can switch from online training to classroom training with nominal fee.
  • 100% placement calls guaranteed till you get placed.
  • Working professional as instructor.
  • Proof of concept (POC) to demonstrate or self-evaluate the concept or theory taught by the instructor.
  • Hands-on Experience with Real-Time Projects.
  • Resume Building & Mock Interviews.
  • Evaluation after each Topic completion.


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About Course

Hadoop framework provides solutions that normally include clusters that are hard to manage and maintain.

In many scenarios, it requires integration with other tools like Sqoop, etc. Hadoop is a big data platform that needs in-depth knowledge that you will learn from a pioneering Hadoop training center in Pune.

You'll work with another popular framework that works with Hadoop i.e. Spark. Spark enables software developers to develop complex, multi-step data application patterns. Where it supports in-memory data sharing across DAG (Directed Acyclic Graph) based applications so that different jobs can work with the same shared data.

  • Introduction to Hadoop Ecosystem
  • Hadoop Setup Installation And Pig Basics
  • Hive Basic, Hive Advanced
  • Map Reduce Basics, POC (Proof Of Concept)
  • Map-reduce Advanced, Hbase Basics
  • Zookeeper, Sqoop, Quick Revision Of Previous Classes
  • Oozie, Hadoop Releases, Introduction To YARN
  • Introduction To Hue, Different Vendors In The Market, Major Project Discussion
  • Spark And Python
  • Hadoop In Cloud Computing: AWS
  • Batches Completed – 70+
  • Students Trained : 2500
  • Real Time Projects
  • Assignment Duration : 30 hrs
  • 50 hrs of career-oriented training with affordable fees.
  • Capstone Projects : Real-world projects from industry experts.
  • Course Completion Certificate with unique verification ID.

The tools and components that we covered in Big Data Hadoop Course are as following

  • Hadoop Ecosystem
  • Spark - Data processing framework to speed up Hadoop jobs
  • Hive - Data Storage
  • Pig - ETL Tool
  • HBase - NoSQL database
  • Oozie - Job scheduler
  • ZooKeeper - User authentication
  • Flume - Data Injection Tool
  • Sqoop - Command-line interface application for transferring data between relational databases and hadoop
  • Python - Programming Language
  • MySQL - Relational database management system
  • MapReduce - Data Processing Paradigm
  • YARN - Resource management and job scheduling technology
  • HUE - Web interface for analyzing data with Apache Hadoop

Training Projects​

Bigdata Hadoop Certification Course Projects

Implementing Big Data Lake for Heterogeneous Data Sources

  • In Bigdata Hadoop project we work on heterogeneous data source system including CSV file format, JSON, database integration (MySQL). In this integration we will learn to get real data from heterogeneous data sources like databases and various file formats.
  • Then we integrate and process with Spark and Load the data in HIVE.
  • Then we work on the staging and data warehouse layer, were we contain or capture recent data, as well as historic data so that unlimited historical data with version control can also be stored in Hadoop.
  • We’ll also work on the INSERT, UPDATE, and DELETE commands using Partition logic on the basis of multiple techniques like date format.


Yes, you can attend demo session before you enroll either we can provide you the recorded lecture so that you can watch it as per your schedule or you can attend live demo lecture either online or offline


Yes, we do provide the placement assistance in which how we work on real time projects will be taught,resume preparation and Job openings will also be provided.More than 80% of candidates have changed their profile by getting either promotion or getting new job offers on good package.


If you miss classes, you can get recording sessions of the lectures.

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