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Big Data – Hadoop

Twenty 19
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In this course you will begin by learning What big data is and the Current challeges in handling Big Data in various domains.Hadoop Distributed File System(HDFS) and its various features are covered in Twenty19’/s online course on Big Data.

  • Fee Details : Rs 2999
  • PROVIDER : Twenty 19

₹ 2,999.00

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Product Description

Too much data ?? Need to process it?? Learn how to analyse Big data using Hadoop – The most happening technology of this century.Learn from scratch what Big Data is and how data can be analysed using Hadoop.The current challenges in handling Big Data in various domains are covered in the chapters of the course.This course contains 3 hours of video content with lab sessions giving the course takers an experience of the interface.Prerequisites : Linux user level understanding and Core java basics

  1. Chapter 1

    • 1- Introduction to Big Data
  2. Chapter 2

    • 2.1- Challenges and Use Cases part 1
    • 2.2- Challenges and Use Cases part 2
  3. Chapter 3

    • 3- Introduction to Hadoop
  4. Chapter 4

    • 4- Hadoop Design Considerations, Components, Stack
  5. Chapter 5

    • 5High Level Architectures, Configuration & Deployment Modes
  6. Chapter 6

    • 6- Hadoop Deployment Modes, Starting Hadoop (commands)
  7. Chapter 7

    • 7- Practical Lab session: Virtualization introduction & installation
  8. Chapter 8

    • 8- Practical Lab session: Configure VM and Hadoop, Start Hadoop
  9. Chapter 9

    • 9- Introduction to HDFS
  10. Chapter 10

    • 10- HDFS: Name Node, EditLog & FsImage, Communication Protocols
  11. Chapter 11

    • 11- HDFS: Client Block diagram, File Read / Write in HDFS, Data Replication
  12. Chapter 12

    • 12- HDFS: Data Node Failure, Name Node Failure, Limitations
  13. Chapter 13

    • 13- HDFS: Filesystem Permission and Shell, permissions, File system commands
  14. Chapter 14

    • 14- HDFS Practical Lab 1 (Practising the HDFS)
  15. Chapter 15

    • 15- HDFS Practical Lab 2 (Programming 1)
  16. Chapter 16

    • 16- HDFS Practical Lab 3 (Programming 2)
  17. Chapter 17

    • 17- Map Reduce
  18. Chapter 18

    • 18- Phases in Map Reduce Framework, MR Architecture, Underlying storage system for MR
  19. Chapter 19

    • 19- Functions in the Map Reduce Model Diagramatic representation for MR and parallel execution
  20. Chapter 20

    • 20- How MR works and how it is used ?
  21. Chapter 21

    • 21- Practical Lab session: First Map Reduce program for word count
  22. Chapter 22

    • 22- Practical Lab session: Second map Reduce program for taking some records with duplicate entries

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