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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

A dual approach

In this book, we will discuss both the building and the management of local Hadoop clusters in addition to showing how to push the processing into the cloud via EMR.

The reason for this is twofold: firstly, though EMR makes Hadoop much more accessible, there are aspects of the technology that only become apparent when manually administering the cluster. Although it is also possible to use EMR in a more manual mode, we'll generally use a local cluster for such explorations. Secondly, though it isn't necessarily an either/or decision, many organizations use a mixture of in-house and cloud-hosted capacities, sometimes due to a concern of over reliance on a single external provider, but practically speaking, it's often convenient to do development and small-scale tests on local capacity and then deploy at production scale into the cloud.

In a few of the later chapters, where we discuss additional products that integrate with Hadoop, we'll mostly give examples of local clusters, as there is no difference between how the products work regardless of where they are deployed.

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