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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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Author (1):
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GABRIELE MODENA GABRIELE MODENA
Author Profile Icon GABRIELE MODENA
GABRIELE MODENA
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Table of Contents (18) Chapters Close

Learning Hadoop 2
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
1. Introduction FREE CHAPTER 2. Storage 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

Other computational frameworks


We've frequently discussed the myriad possibilities brought to the Hadoop platform by YARN. We went into details of two new models, Samza and Spark. Additionally, other more established frameworks such as Pig are also being ported to the framework.

To give a view of the much bigger picture in this section, we will illustrate the breadth of processing possible using YARN by presenting a set of computational models that are currently being ported to Hadoop on top of YARN.

Apache Storm

Storm (http://storm.apache.org) is a distributed computation framework written (mainly) in the Clojure programming language. It uses custom-created spouts and bolts to define information sources and manipulations to allow distributed processing of streaming data. A Storm application is designed as a topology of interfaces that creates a stream of transformations. It provides similar functionality to a MapReduce job with the exception that the topology will theoretically run indefinitely...

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