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Apache Hadoop 3 Quick Start Guide

You're reading from   Apache Hadoop 3 Quick Start Guide Learn about big data processing and analytics

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Product type Paperback
Published in Oct 2018
Publisher Packt
ISBN-13 9781788999830
Length 220 pages
Edition 1st Edition
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Author (1):
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Hrishikesh Vijay Karambelkar Hrishikesh Vijay Karambelkar
Author Profile Icon Hrishikesh Vijay Karambelkar
Hrishikesh Vijay Karambelkar
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Table of Contents (10) Chapters Close

Preface 1. Hadoop 3.0 - Background and Introduction 2. Planning and Setting Up Hadoop Clusters FREE CHAPTER 3. Deep Dive into the Hadoop Distributed File System 4. Developing MapReduce Applications 5. Building Rich YARN Applications 6. Monitoring and Administration of a Hadoop Cluster 7. Demystifying Hadoop Ecosystem Components 8. Advanced Topics in Apache Hadoop 9. Other Books You May Enjoy

Setting up a MapReduce project

In this section, we will learn how to create the environment to start writing applications for MapReduce programming. The programming is typically done in Java. The development of a MapReduce application follows standard Java development principles as follows:

  1. Usually, developers write the programs in a development environment such as Eclipse or NetBeans.
  2. Developers do unit testing usually with a small subset of data. In case of failure, they can run an IDE Debugger to do fault identification.
  3. It is then packaged in JAR files and is tested in a standalone fashion for functionality.
  4. Developers should ideally write unit test cases to test each functionality.
  5. Once it is tested in standalone mode, developers should test it in a cluster or pseudo-distributed environment with full datasets. This will expose more problems, and they can be fixed. Here debugging...
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