Certified Hadoop and Spark Developer Training Course

Course Demo for Hadoop and Spark

A perfect blend of in-depth Hadoop and Spark theoretical knowledge and strong practical skills via implementation of real-time Hadoop and Spark projects to give you a headstart and enable you to bag top Hadoop jobs in the Big Data industry.

★★★★★ Reviews | learn Big Data 42169 Learners

Why should you learn Hadoop and Spark?

The Hadoop market will reach almost $99B by 2022 at the CAGR of around 42%
More than 77% of organizations consider Big Data a top priority
-Peer Research
The average salary of all Hadoop developers today is the amount of $135k
The world’s most valuable resource is Big Data, no longer oil.
-The Economist

Upcoming Batches for Hadoop and Spark Course

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Self-Pacedv/s Live Course Whenever you’d like 70 Hrs Rs. 17990 | $356
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What will you take home from this Hadoop and Spark Online course?

  • Shape your career as Big Data shapes the IT World
  • Grasp concepts of Hadoop and its ecosystem components
  • Become adept in the latest version of Apache Hadoop
  • Develop a complex game-changing MapReduce application
  • Master the Hadoop ecosystem components
  • Grasp the concepts of Apache Spark and its components
  • Acquire an understanding of Spark SQL and Spark MLlib
  • Become capable of clearing the CCA175 Spark and Hadoop developer certifications
  • Enforce best practices for Hadoop and Spark development
  • Gain in-depth Spark practical knowledge
  • Work on live projects on Big Data analytics to get hands-on Experience

What to do before you begin your Spark Hadoop online training?

Although if you’d like, you can brush up on your Java skills with our complementary Java and Storm courses right in your LMS.

Big Data Hadoop Course Prerequisites - DataFlair

Spark Hadoop Training Course Curriculum

Tools to learn in Hadoop Spark Course
  1. Necessity of Big Data and Hadoop in the industry
  2. Paradigm shift - why the industry is shifting to Big Data tools
  3. Different dimensions of Big Data
  4. Data explosion in the Big Data industry
  5. Various implementations of Big Data
  6. Different technologies to handle Big Data
  7. Traditional systems and associated problems
  8. Future of Big Data in the IT industry
  1. Why Hadoop is at the heart of every Big Data solution
  2. Introduction to the Big Data Hadoop framework
  3. Hadoop architecture and design principles
  4. Ingredients of Hadoop
  5. Hadoop characteristics and data-flow
  6. Components of the Hadoop ecosystem
  7. Hadoop Flavors – Apache, Cloudera, Hortonworks, and more
Setup and Installation of single-node Hadoop cluster
  1. Hadoop environment setup and pre-requisites
  2. Hadoop Installation and configuration
  3. Working with Hadoop in pseudo-distributed mode
  4. Troubleshooting encountered problems
Setup and Installation of Hadoop multi-node cluster
  1. Hadoop environment setup on the cloud (Amazon cloud)
  2. Installation of Hadoop pre-requisites on all nodes
  3. Configuration of masters and slaves on the cluster
  4. Playing with Hadoop in distributed mode
  1. What is HDFS (Hadoop Distributed File System)
  2. HDFS daemons and architecture
  3. HDFS data flow and storage mechanism
  4. Hadoop HDFS characteristics and design principles
  5. Responsibility of 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. Expanding clusters in real-time
  14. Hadoop HDFS Web UI and HDFS explorer
  15. HDFS best practices and hardware discussion
  1. What is MapReduce, the processing layer of Hadoop
  2. The need for a distributed processing framework
  3. Issues before MapReduce and its evolution
  4. List processing concepts
  5. Components of MapReduce – Mapper and Reducer
  6. MapReduce terminologies- keys, values, lists, and more
  7. Hadoop MapReduce execution flow
  8. Mapping and reducing data based on keys
  9. MapReduce word-count example to understand the flow
  10. Execution of Map and Reduce together
  11. Controlling the flow of mappers and reducers
  12. Optimization of MapReduce Jobs
  13. Fault-tolerance and data locality
  14. Working with map-only jobs
  15. Introduction to Combiners in MapReduce
  16. How MR jobs can be optimized using combiners
  1. Anatomy of MapReduce
  2. Hadoop MapReduce data types
  3. Developing custom data types using Writable & WritableComparable
  4. InputFormats in MapReduce
  5. InputSplit as a unit of work
  6. How Partitioners partition data
  7. Customization of RecordReader
  8. Moving data 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
  1. The need for an adhoc SQL based solution – Apache Hive
  2. Introduction to and architecture of Hadoop Hive
  3. Playing with the Hive shell and running HQL queries
  4. Hive DDL and DML operations
  5. Hive execution flow
  6. Schema design and other Hive operations
  7. Schema-on-Read vs Schema-on-Write in Hive
  8. Meta-store management and the need for RDBMS
  9. Limitations of the default meta-store
  10. Using SerDe to handle different types of data
  11. Optimization of performance using partitioning
  12. Different Hive applications and use cases
  1. The need for a high level query language - Apache Pig
  2. How Pig complements Hadoop with a scripting language
  3. What is Pig
  4. Pig execution flow
  5. Different Pig operations like filter and join
  6. Compilation of Pig code into MapReduce
  7. Comparison - Pig vs MapReduce
  1. NoSQL databases and their need in the industry
  2. Introduction to Apache HBase
  3. Internals of the HBase architecture
  4. The HBase Master and Slave Model
  5. Column-oriented, 3-dimensional, schema-less datastores
  6. Data modeling in Hadoop HBase
  7. Storing multiple versions of data
  8. Data high-availability and reliability
  9. Comparison - HBase vs HDFS
  10. Comparison - HBase vs RDBMS
  11. Data access mechanisms
  12. Work with HBase using the shell
  1. The need for Apache Sqoop
  2. Introduction and working of Sqoop
  3. Importing data from RDBMS to HDFS
  4. Exporting data to RDBMS from HDFS
  5. Conversion of data import/export queries into MapReduce jobs
  1. What is Apache Flume
  2. Flume architecture and aggregation flow
  3. Understanding Flume components like data Sources and Sinks
  4. Flume channels to buffer events
  5. Reliable & scalable data collection tools
  6. Aggregating streams using Fan-in
  7. Separating streams using Fan-out
  8. Internals of the agent architecture
  9. Production architecture of Flume
  10. Collecting data from different sources to Hadoop HDFS
  11. Multi-tier Flume flow for collection of volumes of data using AVRO
  1. The need for and the evolution of YARN
  2. YARN and its eco-system
  3. YARN daemon architecture
  4. Master of YARN – Resource Manager
  5. Slave of YARN – Node Manager
  6. Requesting resources from the application master
  7. Dynamic slots (containers)
  8. Application execution flow
  9. MapReduce version 2 application over Yarn
  10. Hadoop Federation and Namenode HA
  1. Introducing Scala
  2. Installation and configuration of Scala
  3. Developing, debugging, and running 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, List, Tuple
  8. Pattern-matching and Regex
  9. Eclipse with Scala plugin
  1. Introduction to OOP - object oriented programming
  2. Different oops concepts
  3. Constructors, getters, setters, singletons; overloading and overriding
  4. Nested Classes and visibility Rules
  5. Functional Structures
  6. Functional programming constructs
  7. Call by Name, Call by Value
  1. Problems with older 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
  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
  1. Spark environment setup
  2. Installing and configuring prerequisites
  3. Installation of Spark in local mode
  4. Troubleshooting encountered problems
  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
  1. Working on the Spark shell
  2. Executing Scala and Java statements in the shell
  3. Understanding SparkContext and the driver
  4. Reading data from local file-system and HDFS
  5. Caching data in memory for further use
  6. Distributed persistence
  7. Spark streaming
  8. Testing and troubleshooting
  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. Loading data and how to create RDD in Spark
  7. Persisting RDD in memory or disk
  8. Pairing operations and key-value in Spark
  9. Hadoop integration with Spark
  10. Apache Spark practicals and workshops
  1. The 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
  1. The 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. The need for different graph processing engine
  7. Graph handling using Apache Spark
  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

Live Apache Spark & Hadoop project using Spark & Hadoop components to solve real-world Big Data problems in Hadoop & Spark.

Awesome Big Data projects you’ll get to build in this Spark and Hadoop course

Web Analytics

Weblogs are web server logs where web servers like Apache record all events along with a remote IP, timestamp, requested resource, referral, user agent, and other such data. The objective is to analyze weblogs to generate insights like user navigation patterns, top referral sites, and highest/lowest traffic-times.

web-analytics project

IVR Data Analysis

Learn to analyze IVR(Interactive Voice Response) data and use it to generate multiple insights. IVR call records are meticulously analyzed to help with optimization of the IVR system in an effort to ensure that maximum calls complete at the IVR itself, leaving no room for the need for a call-center.

IVR data analysis project

Set Top Box Data Analysis

Learn to analyze Set-Top-Box data and generate insights about smart tv usage patterns. Analyze set top box media data and generate patterns of channel navigation and VOD. This Spark Project includes details about users’ activities tuning a channel or duration, browsing for videos, or purchasing videos using VOD.

Set Top Box Data Analysis Project

Sentiment Analysis

Sentiment analysis is the analysis of people’s opinions, sentiments, evaluations, appraisals, attitudes, and emotions in relation to entities such as individuals, products, events, services, organizations, and topics. It is achieved by classifying the observed expressions as opinions that may be positive or negative.

sentiment analysis project

Titanic Data Analysis

Titanic was one of the most colossal disasters in the history of mankind, and it happened because of both natural events and human mistakes. The objective of this project is to analyze multiple Titanic data sets to generate essential insights pertaining to age, gender, survived, class, and embarked.

titanic data analysis project

YouTube Data Analysis

Learn to analyze YouTube Data and generate insights like the 10 topmost videos in various categories, user demographics, no. of views, ratings and such. The data holds fields like id, age, category, length, views, ratings, and comments.

YouTube Data Analysis Project

Crime Analysis

Learn to analyze US crime data and find the most crime-prone areas along with the time of crime and its type. The objective is to analyze crime data and generate patterns like time of crime, district, type of crime, latitude, and longitude. This is to ensure that additional security measures can be taken in crime-prone areas.

crime analysis project

Amazon Data Analysis

Amazon data sets are made of users’ reviews and ratings of products and services. Analyzing review data, companies attempt to process the sentiments of their users regarding their products to help improve the same.

Amazon Data Analysis Project

Implementation of the Hadoop and Spark projects in different domains like retail, telecom, media, etc..
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    Features of Hadoop Spark Online Course

    Benefits Hadoop Spark 1-01
    Benefits Hadoop Spark 2-01

    Is this online Hadoop Spark course for you?

    Big Data is the truth of today and Hadoop and Spark prove to be efficient in processing it. So while anyone can benefit from a career in it, here are the kind of professionals who go for this Hadoop and Spark course:

    • Software developers, project managers, and architects Software developers, project managers, and architects
    • BI, ETL icon BI, ETL and Data Warehousing professionals
    • Mainframe and Testing logo Mainframe and testing professionals
    • Business analysts logo Business analysts and analytics professionals
    • DBAs and DB icon DBAs and DB professionals
    • Data Science icon Professionals willing to learn Data Science techniques
    • Big Data career logo Any graduate focusing to build a career in Apache Spark and Scala
    Still can’t decide? Let our Hadoop Spark experts answer your questions

      Free counseling- DataFlair

      Learn Hadoop and Spark the way you like

      Features Self-Paced Pro Course
      Rs. 17990 | $327
      Rs. 9990 | $198
      Live Instructor-Led Course
      Rs. 37990 | $691
      Rs. 23990 | $436
      Course mode Video Based Live Online with Trainer
      Course Objective Express Learning Job readiness
      Extensive hands-on practicals In recordings & in LMS Live with instructor & in LMS
      No. of Projects Ten Ten
      Doubt Clearance Through discussion forum In regular sessions
      Complementary Courses Java Java
      1 live session with Instructor Yes, after course completion In regular sessions
      Lifetime Access
      Discussion Forum Access
      Complementary Job Assistance
      Resume & Interview Preparation
      Personalized career guidance from instructor
      Enroll Now
      Rs. 9990 | $163
      Enroll Now
      Rs. 22990 | $418

      We’re here to help you find the best Spark and Hadoop jobs

      Once you finish this online Hadoop Spark course, our Hadoop job grooming program will help you build your resume while also furthering it to prospective employers. Our mock interviews will help you better understand the interview psychology so you go in prepared.

      Job grooming Hadoop Spark

      Companies you can expect when you get Hadoop-Spark-certified with us

      Hadoop Certification - Companies opportunities - DataFlair

      Spark and Hadoop Training FAQs

      If you miss any session, 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 Hadoop and Spark batch running parallely.

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

      All the Hadoop Spark training sessions will be recorded and you will have lifetime access to the recordings along with the complete Hadoop study material, POCs, Hadoop project etc.

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

      If you have any doubts during Spark Hadoop sessions, you can clear it with 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 around 15 minutes in doubt clearing. Post training, you can post your query over discussion forum and our support team will assist you. Still if you are not comfortable, you can drop mail to instructor or directly interact with him.

      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.

      Our Certified Hadoop Spark 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.

      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.

      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.

      This Spark & Hadoop course is completely online training course with a batch size of 10-12 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.

      Still got questions?Write to us

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