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Home Courses 00 AI & DATA SCIENCE MapReduce – Advanced
Home Courses 00 AI & DATA SCIENCE MapReduce – Advanced

MapReduce - Advanced

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What you'll get

  • 5h 35m
  • 30 Videos
  • Course Level - Beginner| English[Auto-generated]
  • Course Completion Certificates
  • One-Year Access
  • Mobile App Access

Curriculum:

    Comprehensive Mapreduce Training:

    Through this course we are going to learn about the Choosing appropriate Hadoop data types, Implementing a custom Hadoop Writable data type, Implementing a custom Hadoop key type, Emitting data of different value types from a mapper, Choosing a suitable Hadoop Input Format for your input data format, Adding support for new input data formats – implementing a custom Input Format, Formatting the results of MapReduce computations – using Hadoop Output Formats. Next we focus on the data partitioning, , Broadcasting and distributing shared resources to tasks in a MapReduce job – Hadoop Distributed Cache, Using Hadoop with legacy applications – Hadoop Streaming, Adding dependencies between MapReduce jobs and using Hadoop counters for reporting custom metrics.

    Course Objective:

    • Understand the concepts of MapReduce and find out how it solves the problems
    • Learn to develop complex Hadoop MapReduce applications

    Target Audience:

    • Analytics Professionals
    • Analytics Students

    Pre- requisites:

    • Basic Computer Knowledge
    • Passion to learn

     

     

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    Course Overview

    Through this course you are going to learn the use of the MapReduce concepts and its usage in developing complex MapReduce applications.

    11
    2 - 6 hours 5h 35m | 30 Videos | 77186 Views | Beginner  Beginner| English[Auto-generated]
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    Comprehensive Mapreduce Training:

    Through this course we are going to learn about the Choosing appropriate Hadoop data types, Implementing a custom Hadoop Writable data type, Implementing a custom Hadoop key type, Emitting data of different value types from a mapper, Choosing a suitable Hadoop Input Format for your input data format, Adding support for new input data formats – implementing a custom Input Format, Formatting the results of MapReduce computations – using Hadoop Output Formats. Next we focus on the data partitioning, , Broadcasting and distributing shared resources to tasks in a MapReduce job – Hadoop Distributed Cache, Using Hadoop with legacy applications – Hadoop Streaming, Adding dependencies between MapReduce jobs and using Hadoop counters for reporting custom metrics.

    Course Objective:

    • Understand the concepts of MapReduce and find out how it solves the problems
    • Learn to develop complex Hadoop MapReduce applications

    Target Audience:

    • Analytics Professionals
    • Analytics Students

    Pre- requisites:

    • Basic Computer Knowledge
    • Passion to learn

     

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