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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

A note on EMR


One of the main benefits of using cloud services such as those offered by Amazon Web Services is that much of the maintenance overhead is borne by the service provider. Elastic MapReduce can create Hadoop clusters tied to the execution of a single task (non-persistent job flows) or allow long-running clusters that can be used for multiple jobs (persistent job flows). When non-persistent job flows are used, the actual mechanics of how the underlying Hadoop cluster is configured and run are largely invisible to the user. Consequently, users employing non-persistent job flows will not need to consider many of the topics in this chapter. If you are using EMR with persistent job flows, many topics (but not all) do become relevant.

We will generally talk about local Hadoop clusters in this chapter. If you need to reconfigure a persistent job flow, use the same Hadoop properties but set them as described in Chapter 3, Writing MapReduce Jobs.

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