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Big Data Hadoop Training

Live Online & Classroom Enterprise Training

Master the vital components of the Hadoop ecosystem including Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Oozie and Flume with SpringPeople’s Big Data Hadoop Training. This course is best suited for professionals seeking to develop and deploy Hadoop applications for their organization

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Key Features
  • Lifetime Access

  • CloudLabs

  • 24x7 Support

  • Real-time code analysis and feedback

  • 100% Money Back Guarantee

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What is Big Data Hadoop Training about?

Gain comprehensive working knowledge of the important Hadoop tools required to become a top Big Data Developer with our  Big Data course. Learn from industry experts how various organizations implement and deploy Hadoop clusters with detailed case studies. You can work on real life big data projects on the cloud to be an industry ready Hadoop expert.

This course is recommended as the foundation course for all professionals looking to develop Hadoop big data applications for their organizations.

What are the objectives of Big Data Hadoop Training ?

At the end of this Big Data course, you will be able to:

  • Internalize vital big data concepts

  • Demonstrate and implement Hive, Hbase, Flume, Sqoop, Oozie and Pig

  • Work on Hadoop Distributed File System (HDFS)

  • Handle Hadoop Deployment

  • Gain expertise on Hadoop Administration and Maintenance

  • Master Map-Reduce techniques

  • Develop Hadoop 2.7 applications using Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop and Flume

Available Training Modes

Live Online Training

18 Hours

Classroom Training


3 Days

Who is Big Data Hadoop Training for?

  • Anyone who wants to develop big data applications using Hadoop
  • Teams getting started or working on Hadoop based projects

What are the prerequisites for Big Data Hadoop Training?

Basic programming knowledge is recommended

Course Outline

  • 1. Introduction to BigData
    • Which data is called as BigData
    • What are business use cases for BigData
    • BigData requirement for traditional Data warehousing and BI space
    • BigData solutions
  • 2. Introduction to Hadoop
    • The amount of data processing in today's life
    • What Hadoop is why it is important
    • Hadoop comparison with traditional systems
    • Hadoop history
    • Hadoop main components and architecture
  • 3. Hadoop Distributed File System (HDFS)
    • HDFS overview and design
    • HDFS architecture
    • HDFS file storage
    • Component failures and recoveries
    • Block placement
    • Balancing the Hadoop cluster
  • 4. Hadoop Deployment
    • Different Hadoop deployment types
    • Hadoop distribution options
    • Hadoop competitors
    • Hadoop installation procedure
    • Distributed cluster architecture
    • Lab: Hadoop Installation
  • 5. Working with HDFS
    • Ways of accessing data in HDFS
    • Common HDFS operations and commands
    • Different HDFS commands
    • Internals of a file read in HDFS
    • Data copying with 'distcp'
    • Lab: Working with HDFS
  • 6. Hadoop Cluster Configuration
    • Hadoop configuration overview and important configuration file
    • Configuration parameters and values
    • HDFS parameters
    • MapReduce parameters
    • Hadoop environment setup
    • 'Include' and 'Exclude' configuration files
    • Lab: MapReduce Performance Tuning
  • 7. Hadoop Administration and Maintenance
    • Namenode/Datanode directory structures and files
    • Filesystem image and Edit log
    • The Checkpoint Procedure
    • Namenode failure and recovery procedure
    • Safe Mode
    • Metadata and Data backup
    • Potential problems and solutions / What to look for
    • Adding and removing nodes
    • Lab: MapReduce Filesystem Recovery
  • 8. Job Scheduling
    • How to schedule Hadoop Jobs on the same cluster
    • Default Hadoop FIFO Schedule
    • Fair Scheduler and its configuration
  • 9. Map-Reduce Abstraction
    • What MapReduce is and why it is popular
    • The Big Picture of the MapReduce
    • MapReduce process and terminology
    • MapReduce components failures and recoveries
    • Working with MapReduce
    • Lab: Working with MapReduce
  • 10. Programming MapReduce Jobs
    • Java MapReduce implementation
    • Map() and Reduce() methods
    • Java MapReduce calling code
    • Lab: Programming Word Count
  • 11. Input\/Output Formats and Conversion Between Different Formats
    • Default Input and Output formats
    • Sequence File structure
    • Sequence File Input and Output formats
    • Sequence File access via Java API and HDS
    • MapFile
    • Lab: Input Format
    • Lab: Format Conversion
  • 12. MapReduce Features
    • Joining Data Sets in MapReduce Jobs
    • How to write a Map-Side Join
    • How to write a Reduce-Side Join
    • MapReduce Counters
    • Built-in and user-defined counters
    • Retrieving MapReduce counters
    • Lab: Map-Side Join
    • Lab: Reduce-Side Join
  • 13. Introduction to Hive, Hbase, Flume, Sqoop, Oozie and Pig
    • Hive as a data warehouse infrastructure
    • Hbase as the Hadoop Database
    • Using Pig as a scripting language for Hadoop
  • 14. Hadoop Case studies
    • How different organizations use Hadoop cluster in their infrastructure

Who is the instructor for this training?

The trainer for this Big Data Hadoop Training has extensive experience in this domain, including years of experience training & mentoring professionals.


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