Bigdata Hadoop Course in Pune

Duration of course: 90 hrs

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

Are you searching for a Bigdata Training institute, particularly a “near me” classroom?

Then look no further than Technogeeks!

With a placement-oriented approach to training, we serve the students in Pune, Pimpri-Chinchwad and online.

Our software training institute‘s mission is to equip students with the necessary skills and knowledge to succeed in the IT industry.

Technogeeks’ 90 hrs of interactive Bigdata training combines industry-based, job-oriented, hands-on, interactive training with assignments and real-time projects.

In the course, you will get in-depth knowledge of Hadoop Ecosystem tools – Python & Spark, Sqoop, HDFS, MapReduce, Hive, HBase, Oozie, ZooKeeper, Pig, Flume, and YARN by working on the Big Data Hadoop project (Implementing a Big Data Lake for Heterogeneous Data Sources).

So if you’re looking for a comprehensive Hadoop training course in Pune, look no further than our Hadoop training center. We’ll give you the skills and knowledge you need to use Hadoop effectively.

The big data Hadoop course is designed by industry experts with in-depth knowledge of Hadoop ecosystem tools and big data.

At our big data institute in Pune, you’ll learn about the Hadoop framework and how to manage and maintain clusters effectively. You’ll also learn about integrating Hadoop with other tools, like Sqoop.

Hadoop is a big data platform that requires in-depth knowledge to use effectively. Our Hadoop training course will give you the knowledge and skills to use Hadoop effectively.

You’ll also learn a popular framework, Spark, that works with Hadoop.

Spark enables software developers to develop complex, multi-step data application patterns. It also supports in-memory data sharing across DAG (Directed Acyclic Graph)-based applications so that different jobs can work with the same shared data.

This course will introduce you to the Hadoop ecosystem and show you how to set up a cluster. We will also cover the basics of the MapReduce programming model and HDFS.

Bigdata Hadoop Training Syllabus

Best Blended Syllabus for Big Data 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 takes 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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About Course

After completing this course, you will be able to:

  • Understand the concept of big data and the Hadoop ecosystem.
  • Install and configure Hadoop on a single node or a multi-node cluster.
  • Understand the internals of the Hadoop Distributed File System (HDFS).
  • Installation & Setup of Pig with basics.
  • Ingest data into HDFS using Sqoop.
  • Process data stored in HDFS using MapReduce.
  • Analyze data stored in HDFS using Hive and HBase.
  • Schedule and monitor Hadoop jobs using Oozie.
  • Use Apache Spark for real-time data processing.
  • Implement a Big Data solution for a real-world problem.
  • Hive basics and advanced Hive to read, write, and manage petabytes of data using SQL.
  • You will use Apache ZooKeeper to add operational services like a distributed configuration service, a synchronization service, and a naming registry for a Hadoop cluster.
  • With YARN, you will learn to run different types of distributed applications for data stored in HDFS for batch processing, stream processing, interactive processing, and graph processing.
  • Introduction To Hue, Different Vendors In The Market, 
  • PySpark to create distributed datasets from any Hadoop-supported storage source.
  • You’ll run AWS services (EMR, Athena, Redshift, Glue) to run Hadoop & Spark to process Big Data In Cloud.
  • 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 – 125+

👉Students - 2500+

👉Learning Mode: Live Interactive Online training, Classroom training in Pune

👉Training hrs - 40 hrs of training

👉Assignments Duration: 30 hrs of Assignments

👉Projects: 20 hrs of Real-time Projects (2 major projects)

👉Modules: 10

👉Services Covered During Training: 20+ Services

👉Course Completion Certificate with unique verification ID

👉Mentor Support: 1:1 Mentorship

👉Resources: Classroom Recordings, Notes, Assignments, Projects, Interview FAQs

👉Tools Covered: (15 tools) Hadoop, HDFS, Pyspark, Hive, Pig, HBase, Oozie, Apache Kafka, Yarn & more.

👉Mock Interview

👉Job Assistance: Telegram channel for placement Assistance:  OR Search for "technogeeks solutions"

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

  • HDFS - The distributed file system that is used to store the data on the Hadoop cluster.

  • Hadoop Common: The common utilities and libraries that are required by all other Hadoop components.

  • 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

In big data hadoop training, you will be introduced to the AWS services like EMR, Athena, Redshift, Glue, and S3.

The  scope of the course is learning the Hadoop ecosystem,  so you will be introduced with theoretical knowledge of these services and how to integrate your Hadoop environment with these services to enable data movement, workflows, and analytics across the wide range of offerings available on the AWS platform.

"Data pipelines" are a collection of processes that collect data from different sources and store it in one location, like a data warehouse or data lake.


A data pipeline connects the whole process of collecting data, turning it into insights and models, sharing insights, and using the model whenever and wherever action is needed to reach a business goal.


With the volume, variety, and speed at which data is changing, data architects and data engineers have had to adjust to "big data."


This data has the potential to be used for different use cases, including, alerting, real-time reporting, and predictive analytics.


This data is collected using existing data pipelines. However, big data pipelines can be used to extract, process, and load (ETL) large amounts of data & considering the three Vs of big data: volume, variety, and speed. The distinction is important because analysts expect that future data output will increase, which requires data pipelines to be scalable, which might fluctuate over time.


With the rapid growth of big data in recent years, it has become more appealing to build streaming data pipelines & interpret data in real time, enabling prompt action. 


Scalable and efficient data pipelines are as important for the success of data analytics, data science, and machine learning as reliable data pipelines are for staying in business.


In practice, many events are likely to happen at the same time or very close together, so the big data pipeline must be able to handle large amounts of data at the same time.


Due to the diversity of big data, large data pipelines must be able to recognize and process data in a wide variety of formats, including structured, unstructured, and semi-structured. 


Data pipelines have five stages grouped into three categories according to efforts needed in each stage

Data Engineering: collection, ingestion, and preparation (~50% effort)

Analytics / Machine Learning: computation (~25% effort)

Delivery: presentation (~25% effort)

Course Benefits​

By enrolling Big Data Hadoop course you get the following benefits

  • 0% Interest installments option.
  • No prerequisite.
  • Pay only after attending one FREE TRIAL OF RECORDED LESSON.
  • Syllabus includes basics to advance Big Data Hadoop topics.
  • In-Person training Live Interactive Sessions conducted by working IT industry professionals industry experts.
  • Comprehensive course covers all aspects of Big Data Hadoop.
  • Carefully selected questions to provide you with all the practice you need during training.
  • Classroom & Online Training – Can switch from online training to classroom training.
  • Tips from working professionals with years of experience in Big data hadoop on how to write clean and reusable code.
  • Course designed for non-IT & IT professionals.
  • Evaluation after each Topic completion.
  • 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 Interview with Technogeeks team.
  • 100% placement assistance that you will get guaranteed interview calls till you get placed.
  • Get shareable completion certification from Technogeeks with a unique identification number.
  • Enroll in weekday or weekend class.
  • Get one year access to class recordings.

Training Projects​

Bigdata Hadoop Certification Course Projects


In the Big Data Hadoop project, we work with different types of data sources, such as CSV files, JSON, and MySQL databases.

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 into HIVE.

Then we work on the staging and data warehouse layers. This is where 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.

Instructor-led Big Data Hadoop Live Online Interactive Training

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Bigdata Hadoop Training Certification From Technogeeks​

The Bigdata Hadoop training completion certificate from Technogeeks will help you in,

  • Career Opportunities in Analytics, Bigdata engineering & more
  • Improving Reputation as skilled professional
  • Competitive Advantage among the cohort
  • Proof of Learning
  • Establishing Professional Credibility

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


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Tools Covered in Big Data Hadoop Training​

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Yes, you can attend a 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 a live demo lecture either online or offline

Yes, we provide placement assistance until you get a placement after course completion.

Technogeeks' placement team will help with job search, resume reviews, interview preparation, and connecting students with potential employers.


Bigdata is a very popular field, and many companies are looking for skilled professionals with knowledge of Hadoop. Hadoop training can be valuable for job seekers, and many companies look for certified professionals. So, having Hadoop knowledge and certification can be a great asset to your career.


It's important to note that Placement assistance is not a job placement guarantee. We will support you with your job search. Your qualifications, experience, and the job market conditions are some of the factors that can affect your job search.

Checkout our Telegram Channel for Placement Assistance:

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

The course is designed for beginners without prior knowledge of big data or Hadoop. However, some basic knowledge of Linux commands, Java, and SQL will be helpful.

Big Data generally refers to the massive amount of data generated and collected from different sources such as social media, sensors, digital devices, etc. Its volume and velocity characterize this data and variety, meaning that it is extremely large, comes in at high speed, and is diverse in structure and format.
Due to its size and complexity, traditional data processing methods are often inadequate for handling Big Data. To tackle this, Big Data has emerged to provide new methodologies, tools, and technologies for analyzing, storing, and processing these large-scale datasets.
It used Big Data analysis to extract valuable insights and information from the data and used it for improving business operations, identifying trends, making data-driven decisions, and more.

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