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Hadoop Beginner's Guide

You're reading from   Hadoop Beginner's Guide Get your mountain of data under control with Hadoop. This guide requires no prior knowledge of the software or cloud services ‚Äì just a willingness to learn the basics from this practical step-by-step tutorial.

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Product type Paperback
Published in Feb 2013
Publisher Packt
ISBN-13 9781849517300
Length 398 pages
Edition 1st Edition
Tools
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Table of Contents (19) Chapters Close

Hadoop Beginner's Guide
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
1. What It's All About 2. Getting Hadoop Up and Running FREE CHAPTER 3. Understanding MapReduce 4. Developing MapReduce Programs 5. Advanced MapReduce Techniques 6. When Things Break 7. Keeping Things Running 8. A Relational View on Data with Hive 9. Working with Relational Databases 10. Data Collection with Flume 11. Where to Go Next Pop Quiz Answers Index

Other programming abstractions


Hadoop is not just extended by additional functionality; there are tools to provide entirely different paradigms for writing the code used to process your data within Hadoop.

Pig

We mentioned Pig (http://pig.apache.org) in Chapter 8, A Relational View on Data with Hive, and won't say much else about it here. Just remember that it is available and may be useful if you have processes or people for whom a data flow definition of the Hadoop processes is a more intuitive or better fit than writing raw MapReduce code or HiveQL scripts. Remember that the major difference is that Pig is an imperative language (it defines how the process will be executed), while Hive is more declarative (defines the desired results but not how they will be produced).

Cascading

Cascading is not an Apache project but is open source and is available at http://www.cascading.org. While Hive and Pig effectively define different languages with which to express data processing, Cascading provides...

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