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Learning Hadoop 2

You're reading from   Learning Hadoop 2 Design and implement data processing, lifecycle management, and analytic workflows with the cutting-edge toolbox of Hadoop 2

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Product type Paperback
Published in Feb 2015
Publisher Packt
ISBN-13 9781783285518
Length 382 pages
Edition 1st Edition
Tools
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Toc

Table of Contents (13) Chapters Close

Preface 1. Introduction 2. Storage FREE CHAPTER 3. Processing – MapReduce and Beyond 4. Real-time Computation with Samza 5. Iterative Computation with Spark 6. Data Analysis with Apache Pig 7. Hadoop and SQL 8. Data Lifecycle Management 9. Making Development Easier 10. Running a Hadoop Cluster 11. Where to Go Next Index

Chapter 3. Processing – MapReduce and Beyond

In Hadoop 1, the platform had two clear components: HDFS for data storage and MapReduce for data processing. The previous chapter described the evolution of HDFS in Hadoop 2 and in this chapter we'll discuss data processing.

The picture with processing in Hadoop 2 has changed more significantly than has storage, and Hadoop now supports multiple processing models as first-class citizens. In this chapter we'll explore both MapReduce and other computational models in Hadoop2. In particular, we'll cover:

  • What MapReduce is and the Java API required to write applications for it
  • How MapReduce is implemented in practice
  • How Hadoop reads data into and out of its processing jobs
  • YARN, the Hadoop2 component that allows processing beyond MapReduce on the platform
  • An introduction to several computational models implemented on YARN
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