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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

Streaming in MapReduce programming

The traditional MapReduce programming requires users to write map and reduction functions as per the specifications of the defined API. However, what if I already have a processing function written, and I want to federate the processing to my own function, still using the MapReduce concept over Hadoop's distributed File System? There is a possibility to solve this with the streaming and pipes functions of Apache Hadoop.

Hadoop streaming allows user to code their logic in any programming language such as C, C++, and Python, and it provides a hook for the custom logic to integrate with traditional MapReduce framework with no or minimal lines of Java code. The Hadoop streaming APIs allow users to run any scripts or executables outside of the traditional Java platform. This capability is similar to Unix's Pipe function (https://en.wikipedia...

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