Certified Hadoop and Spark Developer Training Course

Hadoop Course Featured Image DataFlair

Hadoop and Spark course from DataFlair is a great blend of in-depth theoretical knowledge and strong practical skills via implementation of real life projects to enable you head start and grab top Big Data jobs in the industry.

70+ Hrs of instructor-led sessions
200+ Hrs of practicals & assignments
10 Real-time big data projects
Lifetime access to course with support
Job oriented course with job assistance

★★★★★Reviews | 24228 Learners

Offers: Buy 1 Study 5. Get Apache Storm & Java courses free with Instructor-led Course

About Big Data Hadoop and Spark Developer Course

Big Data Hadoop Spark online training course is designed by certified Hadoop Spark developers as per industry standards and need to make you quite apt to grab top jobs and start your career as Big Data developer as thousands of other professionals have already done by joining this Hadoop Spark combo course.

Become Hadoop Spark expert by learning core Big Data technologies and gain hands-on knowledge of Hadoop and Spark along with their eco-system components like HDFS, Map-Reduce, Hive, Pig, HBase, Sqoop, Flume, Yarn, core Spark, Spark RDDs, Apache Spark SQL, Spark MLlib, Spark GraphX and Spark Streaming through this Spark Hadoop developer course. For extensive hands-on, individual topics are explained using multiple workshops. The online Big data Hadoop and Spark certification course also covers real life use-cases, multiple POCs, live Hadoop and Spark project to help you in grabbing top Big Data Hadoop Spark jobs in the industry.


Objectives of Big Data Hadoop and Spark online Training

  1. Bring shift in your career as Big data has brought in IT world
  2. Grasp the concepts of hadoop and its ecosystem components
  3. Become adept in latest version of Apache Hadoop
  4. Develop complex Game-Changing MapReduce Application
  5. Master the hadoop ecosystem components
  6. Grasp the concepts of Apache Spark and its components
  7. Acquire understanding of Spark SQL and Spark MLlib
  8. Become capable of clearing CCA175 Spark and Hadoop developer certification
  9. Enforce best practices for Hadoop and Spark development
  10. Gain in depth Spark practical knowledge
  11. Work on live projects on Big Data analytics to get hands-on Experience


Upcoming Batch Schedule

17 Dec 09.00 PM – 11.00 PM IST
7.30 AM – 09.30 AM PST
Mon-Fri 70 Hrs
22 Dec 8.00 PM – 11.00 PM IST
09.30 AM – 12.30 PM EST
Sat-Sun 70 Hrs
14 Jan 09.00 PM – 11.00 PM IST
7.30 AM – 09.30 AM PST
Mon-Fri 70 Hrs
12 Jan 10.00 AM – 01.00 PM IST
08.30 PM – 11.30 PM PST
Sat-Sun 70 Hrs
9 Feb 8.00 PM – 11.00 PM IST
09.30 AM – 12.30 PM EST
Sat-Sun 70 Hrs

Why you should learn Hadoop and Spark

Hadoop Salary
Average salary of Hadoop & Spark Developers is $135k -Indeed

Shortage of Hadoop talent
There will be a shortage of 1.5M Big Data experts by 2018 -McKinsey

Hadoop market trends
Big Data market will reach $99B by 2022 at the CAGR of 42% -Forbes

Hadoop company priority
More than 77% of organizations consider Big Data a top priority -Peer Research

What will you get from this Apache Spark Hadoop Course

live online instructor-led Hadoop training
70+ hrs of live online instructor-led sessions by industry experts

Hadoop Certification
Industry renowned Spark and Hadoop certification to boost your resume

practicals, workshops, labs and assignments
200+ hrs of Hadoop & Spark practicals, workshops, labs, quiz and assignments

Hadoop career discussion
Personalized one to one career discussion directly with the trainer

Real life case studies and live Big data project
Real life Big Data case studies and live hadoop spark project to solve real problem

resume preparation and Hadoop interviews
Mock interview & resume preparation to excel in Big data interviews

Lifetime access to Hadoop and Big Data Course
Lifetime access to Hadoop spark course, recorded sessions and study materials

job assistance and Hadoop career
Premium Big Data job assistance and support to step ahead in the career

Hadoop discussion forum
Discussion forum for resolving your queries and interacting with fellow batch-mates

course auto upgradation
Auto Upgradation of the training course as well as material to the latest versions

Who should go for this online Spark Hadoop Course

YOU, yes you should go for this Big Data Hadoop Spark online combo course if you want to take a leap in your career as Big data developer. This course will be useful for:

  1. Software developers, Project Managers and architects
  2. BI, ETL and Data Warehousing Professionals
  3. Mainframe and testing Professionals
  4. Business analysts and Analytics professionals
  5. DBAs and DB professionals
  6. Professionals willing to learn Data Science techniques
  7. Any graduate focusing to build career in Big Data


Pre-requisites to attend Hadoop and Spark course online course

As such no prior knowledge of any technology is required to learn Big Data Spark and Hadoop. In case you feel any need to revise your Java concepts, Java course will be provided in your LMS as complimentary with Big Data Hadoop Spark tutorial course.

Highly experienced instructors
1 to 1 interaction with the instructor
10 Real time Big Data projects
100% Job assistance and support
Lifetime access to the course

Hadoop & Spark Course Curriculum

1. Big Picture of Big Data
  1. Big Data and its necessity
  2. Paradigm Shift - why industry is shifting to Big Data tools
  3. Different dimensions of Big Data
  4. Data explosion in industry
  5. Various implementations of Big Data
  6. Technologies for handling Big Data
  7. Traditional systems and associated problems
  8. Future of Big Data in IT industry
2. Introduction to Hadoop
  1. Why Hadoop is at the heart of every Big Data solution
  2. Hadoop framework Introduction
  3. Architecture and design principles of Hadoop
  4. Ingredients of Hadoop
  5. Hadoop characteristics and data-flow
  6. Components of Hadoop ecosystem
  7. Hadoop Flavors – Apache, Cloudera, Hortonworks etc.
3. Setup and Installation of Hadoop

Setup and Installation of Single-Node Hadoop Cluster

  1. Setup of Hadoop environment and pre-requisites
  2. Installation and configuration of Hadoop
  3. Work with Hadoop in pseudo-distributed mode
  4. Troubleshooting the encountered problems

Setup and Installation of Hadoop multi-node Cluster

  1. Setup Hadoop environment on the cloud (Amazon cloud)
  2. Install Hadoop pre-requisites on all the nodes
  3. Configuration of Masters and Slaves on Cluster
  4. Hadoop in distributed mode
4. HDFS – Storage Layer
  1. Introduction to HDFS - Hadoop Distributed File System
  2. HDFS Architecture and daemons
  3. HDFS data flow and its storage mechanism
  4. HDFS Characteristics and design principles
  5. Responsibility of Hadoop HDFS Master – NameNode
  6. Storage mechanism of Hadoop meta-data
  7. Work of HDFS Slaves – DataNodes
  8. Data Blocks and distributed storage
  9. Replication of blocks, reliability and high availability
  10. Rack-awareness, Scalability and other features
  11. Different HDFS APIs and terminologies
  12. Commissioning of nodes and addition of more nodes
  13. Expand the cluster in real-time
  14. Hadoop HDFS web UI and HDFS explorer
  15. HDFS Best Practices and hardware discussion
5. Deep Dive into MapReduce
  1. Introduction to MapReduce - Processing layer of Hadoop
  2. Need of distributed processing framework
  3. Issues before evolution of MapReduce
  4. List processing Concepts
  5. MapReduce components – Mapper and Reducer
  6. MapReduce terminologies key, values, lists etc.
  7. Hadoop MapReduce execution flow
  8. Mapping data and reducing them based on keys
  9. MapReduce word-count example to understand the flow
  10. Execution of Map and Reduce together
  11. Control the flow of mappers and reducers
  12. MapReduce Job Optimization
  13. Fault-tolerance and data locality in MapReduce
  14. Work with map-only jobs
  15. Introduction to Combiners in MapReduce
  16. How MR jobs can be optimized using Combiners
6. MapReduce - Advanced Concepts
  1. Anatomy of MapReduce
  2. MapReduce data-types
  3. Develop custom data-types using Writable & WritableComparable
  4. InputFormats in Hadoop MapReduce
  5. How InputSplit is unit of work
  6. Data partitioning using Partitioners
  7. Customization of RecordReader
  8. Data movement from mapper to reducer – shuffling & sorting
  9. Distributed Cache and job chaining
  10. Different Hadoop case-studies to customize each component
  11. Job scheduling in MapReduce
7. Hive – Data Analysis Tool
  1. Need of adhoc SQL based solution – Apache Hive
  2. Hive Introduction and architecture
  3. Play with Hive shell and run HQL queries
  4. DDL and DML operations in Hive
  5. Execution flow in Hive
  6. Schema Design and other Hive operations
  7. Schema on read vs Schema on write in Hive
  8. Meta-store management and need of RDBMS
  9. Limitation of default meta-store
  10. Serde to handle different types of data
  11. Performance Optimization using partitioning
  12. Hive applications and use cases
8. Pig - Data Analysis Tool
  1. Need of high level query language - Apache Pig
  2. How pig complements Hadoop with scripting language
  3. Introduction to Pig
  4. Pig execution flow
  5. Different operations in Pig like filter and join
  6. Compilation of pig code into MapReduce
  7. Comparison between MapReduce vs Pig
9. NoSQL Database - HBase
  1. Need of NoSQL Databases in the industry
  2. Introduction to Apache HBase
  3. HBase architecture - master and slave model
  4. Data modeling in Hadoop HBase
  5. Store multiple versions of data
  6. Data high-availability and reliability in HBase
  7. Comparison between HBase vs HDFS
  8. Comparison between RDBMS vs HBase
  9. Data access mechanism in HBase
  10. Work with HBase using shell
10. Data Collection using Sqoop
  1. Introduction to Apache Sqoop and its need
  2. Working of Sqoop
  3. Import data from RDBMS to HDFS
  4. Export data to RDBMS from HDFS
  5. Conversion of data import / export query into MapReduce job
11. Data Collection using Flume
  1. Introduction to Apache Flume
  2. Architecture and aggregation flow in Flume
  3. Understand Flume components like data Source and Sink
  4. Flume channels to buffer the events
  5. Reliable & scalable data collection tool
  6. Aggregate streams using Fan-in
  7. Separate streams using Fan-out
  8. Internals of agent architecture
  9. Flume Production architecture
  10. Collect data from different sources to Hadoop HDFS
  11. Multi-tier flume flow for collection of volumes of data using avro
12. Apache Yarn & Advanced concepts in latest version
  1. Need and evolution of Yarn
  2. Introduction to Yarn and its eco-system
  3. Yarn daemon architecture
  4. Master of Yarn – Resource Manager
  5. Slave of Yarn – Node Manager
  6. Resource request from Application master
  7. Dynamic slots called containers
  8. YARN Application execution flow
  9. MapReduce version 2 application over Yarn
  10. Hadoop Federation and Namenode HA
1. Dive into Scala
  1. Introduction to Scala
  2. Installation and configuration of Scala
  3. Develop, debug and run basic Scala Programs
  4. Various Scala operations
  5. Functions and procedures in Scala
  6. Scala APIs for common operations
  7. Loops and collections Array, Map, Lists, Tuples
  8. Pattern matching & Regex
  9. Eclipse with Scala plugin
2. Object Oriented and Functional Programming
  1. Introduction to OOP - object oriented programming
  2. Different oops concepts
  3. Constructor, getter, setter, singleton, overloading and overriding
  4. Nested Classes, Visibility Rules
  5. Functional Structures
  6. Functional programming constructs
  7. Call by Name, Call by Value
3. Big Data and need for Spark
  1. Problems with old Big Data solutions
  2. Batch vs Real-time vs in-Memory processing
  3. Limitations of MapReduce
  4. Apache Storm introduction and its limitations
  5. Need for Apache Spark
4. Deep Dive in Apache Spark
  1. Introduction to Apache Spark
  2. Architecture and design principles of Apache Spark
  3. Spark Features and characteristics
  4. Apache Spark Ecosystem components and their insights
5. Deploy Spark in Local mode
  1. Spark Environment setup
  2. Install and configure prerequisites
  3. Installation of Spark in local mode
  4. Troubleshooting the encountered problems
6. Apache Spark deployment in different modes
  1. Spark installation and configuration in standalone mode
  2. Installation and configuration of Spark in YARN mode
  3. Installation and configuration of Spark on a real cluster
  4. Best practices for Spark deployment
7. Demystify Apache Spark
  1. Work on Spark shell
  2. Execute Scala and Java statements in shell
  3. Understand SparkContext and driver
  4. Read data from local file-system and HDFS
  5. Cache the data in memory for further use
  6. Distributed persistence
  7. Spark streaming
  8. Testing and troubleshooting
8. Deep dive into Spark RDD
  1. Introduction to Spark RDDs
  2. How RDDs make Spark a feature rich framework
  3. Transformations in Spark RDDs
  4. Spark RDDs action and persistence
  5. Lazy operations and fault tolerance in Spark
  6. Load data and how to create RDD in Spark
  7. Persist RDD in memory or disk
  8. Pair operations and key-value in Spark
  9. Hadoop integration with Spark
  10. Apache Spark practicals and workshops
9. Spark streaming
  1. Need for stream analytics
  2. Comparison with Storm and S4
  3. Real-time data processing using streaming
  4. Fault tolerance and checkpointing in Spark
  5. Stateful Stream Processing
  6. DStream and window operations in Spark
  7. Spark Stream execution flow
  8. Connection to various source systems
  9. Performance optimizations in Spark
10. Spark MLlib and Spark GraphX
  1. Need for Spark machine learning
  2. Introduction to Machine learning in Spark
  3. Various Spark libraries
  4. Algorithms for clustering, statistical analytics, classification etc.
  5. Introduction to Spark GraphX
  6. Need for different graph processing engine
  7. Graph handling using Apache Spark
11. Spark-SQL
  1. Introduction to Spark SQL
  2. Apache Spark SQL Features and Data flow
  3. Architecture and components of Spark SQL
  4. Hive and Spark together
  5. Data frames and loading data
  6. Hive Queries through Spark
  7. Various Spark DDL and DML operations
  8. Performance tuning in Spark
12. Real Life Hadoop & Spark Project
Live Apache Spark & Hadoop project using Spark & Hadoop components to solve real-world Big Data problems in Hadoop & Spark.

Apache Hadoop & Spark Projects


Web Analytics

Weblogs are web server logs, where web servers like apache records all the events along with remote-IP, time-stamp, requested-resource, referral, user-agent, etc. The objective is to analyze the weblogs and generate insights like user navigation pattern, top referral sites, highest/lowest traffic-time, etc.



Sentiment Analysis

Sentiment analysis is the analysis of people’s opinions, sentiments, evaluations, appraisals, attitudes and emotions in relation to entities like individuals, products, events, services, organizations and topics by classifying the expressions as negative / positive opinions



Crime Analysis

Analyze the US crime data and find most crime-prone area along with crime time and its type. The objective is to analyze the crime data and generate crime patterns like time, district, crime-type, latitude, longitude, etc. So that additional security measures can be taken in crime prone area.



IVR Data Analysis

Analyze IVR (Interactive Voice Response) data and generate various insights. The IVR call records are analyzed to optimize to IVR system so that maximum calls are completed at IVR and there will be minimum need for Call-center.



Titanic Data Analysis

Titanic was one of the biggest disasters in the history of mankind, which happened due to natural events and human mistakes. The objective is to analyze Titanic data sets and generate various insights related to age, gender, survived, class, emabrked, etc.



Amazon Data Analysis

Amazon data-sets contains user-reviews of different products, services, star-ratings, etc. The objective of the project is to analyze the users’ review data, companies can analyze the sentiments of the users regarding their products and use it for betterment of the same.



Set Top Box Data Analysis

Analyze Set Top Box data and generate various insights about the smart tv usage pattern. The objective of the project is to analyze set top box media data and generate patterns of channel navigation, VOD, etc.. The data contains details about users’ activities like tuning a channel, duration, browsing for videos, purchase video using VOD (video on demand), etc.



Youtube Data Analysis

Analyze the YouTube Data and generate insights like top 10 most videos in various categories, User demographics, no. of views, ratings etc. The data contains fields like Id, Age, Category, Length, Views, ratings, comments, etc.


Course Plans

Self-Paced Pro Course
Rs. 8990 | $163

Video Based

Yes, in recordings & in LMS





Through discussion forum

Yes, post course completion

Java, with lifetime access





Express Learning

Live Instructor-Led Course
Rs. 22990 | $418

Live Online with Trainer

Yes, live with instructor & in LMS


Yes, with support



In regular sessions

Yes, post course completion

Java & Storm, with lifetime access



100% interactive classes

Yes, from instructor

Job readiness

Job Grooming

On completion of Hadoop & Spark training course, DataFlair’s job grooming program will help you in resume building and interview preparation. Mock interviews and resume referrals will make you job ready to excel in the interviews.

resume building
Resume Building

Build a favourable impression with the resume that stands out.

Resume Referral
Resume Referral

Get connected with top employers to boost your career prospects.

Mock Interview
Mock Interview

Make yourself job ready with multiple in-depth mock interviews.

Job Ready
Job Readiness

Get ready to work from day one with multiple projects & best practices

Companies you could land up with
Companies you could land

Corporate Clients /

Offers made to

Projects developed
by students

Hours of classes

Customer Reviews

Hadoop & Spark Training FAQs

How will you help me if I miss any session?

If you miss any session of Hadoop Spark training, you need not worry as recordings will be uploaded in LMS immediately as the session gets over. You can go through it and get your queries cleared from the instructor during next session. You can also ask him to explain the concepts that you did not understand and were covered in session you missed. Alternatively, you can attend the missed session in any other batch of Hadoop & Spark running in parallel.

How will I do Hadoop Spark practicals at home?

Instructor will help you in setting the virtual machine on your own system at which you can do the entire Spark & Hadoop practicals at anytime from anywhere. Manuals to set virtual machine will be available in your LMS in case you want to go through the steps again. For Hadoop & Spark practicals virtual machine can be set on MAC or Windows machine also.

How long will the course recording available with me?

All the sessions of Big Data course will be recorded and you will have lifetime access to the recordings along with the complete study material, documents, codes, POCs, project etc.

What things do I need to attend Spark Hadoop online classes?

To attend Spark Hadoop online training, you just need a laptop or PC with a good internet connection of around 1 MBPS (But lesser speed of 512 KBPS will also work). The broadband connection is recommended but you can connect through data card as well.

How can I get my doubts cleared post Spark Hadoop class?

If you have any doubt during any Spark Hadoop session, you can get it cleared from the instructor immediately. If you get queries after the session, you can get it cleared from the instructor in the next session as before starting any session, instructor spends sufficient time in doubt clearing. Post training, you can post your query over Big Data discussion forum and our support team will assist you. Still, if you are not comfortable, you can drop mail to the instructor or directly interact with him.

What is the system specifications required for learning Hadoop & Spark?

Recommended is minimum of i3 processor, 20 GB disk and 4 GB RAM in order to learn Big Data, Spark, and Hadoop, although students have learnt Hadoop & Spark on 3 GB RAM as well.

How this Big Data training will help me in getting job?

Our Certified Big Data training Course includes multiple workshops, POCs, project etc. that will prepare you to the level that you can start working from day 1 wherever you go. You will be assisted in resume preparation. Mock interview will help you in getting ready to face interviews. We will also guide you with the job openings matching to your resume. All this will help you in landing your dream job in Big Data industry.

What will be the end result of doing this course?

You will be skilled with the practical and theoretical knowledge that industry is looking for and will become certified Hadoop & Spark professional who is ready to take Big Data Projects in top organizations.

How will I be able to interact with the instructor during training?

Both voice and chat will be enabled during the big data Hadoop & Spark training course. You can talk with the instructor or can also interact via chatting.

Is this Hadoop Spark classroom training or online training?

This Spark & Hadoop course is completely online training course with a batch size of 8-10 students only. You will be able to interact with the trainer through voice or chat and individual attention will be provided to all. The trainer ensures that every student is clear of all the concepts taught before proceeding ahead. So there will be a complete environment of classroom learning.

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