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Mastering Hadoop

You're reading from   Mastering Hadoop Go beyond the basics and master the next generation of Hadoop data processing platforms

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Product type Paperback
Published in Dec 2014
Publisher
ISBN-13 9781783983643
Length 374 pages
Edition 1st Edition
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Author (1):
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Sandeep Karanth Sandeep Karanth
Author Profile Icon Sandeep Karanth
Sandeep Karanth
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Toc

Table of Contents (15) Chapters Close

Preface 1. Hadoop 2.X FREE CHAPTER 2. Advanced MapReduce 3. Advanced Pig 4. Advanced Hive 5. Serialization and Hadoop I/O 6. YARN – Bringing Other Paradigms to Hadoop 7. Storm on YARN – Low Latency Processing in Hadoop 8. Hadoop on the Cloud 9. HDFS Replacements 10. HDFS Federation 11. Hadoop Security 12. Analytics Using Hadoop A. Hadoop for Microsoft Windows Index

Apache Mahout

Apache Mahout is a scalable machine learning library. It is an open source library under the Apache Software Foundation. It supports algorithms for clustering, classification, and collaborative filtering on distributed platforms. Apache Mahout welcomes contributors to contribute any algorithm to the library. The algorithm coded may not always be distributed and can run on a single machine as well.

Tip

As Apache Mahout allows developers to introduce single-machine algorithms, it is recommended that you study the implementation before running it on Hadoop.

Apache Mahout has a few algorithms that are implemented as MapReduce. These algorithms can be run in Hadoop to exploit the parallelism on a distributed cluster. Again, a word of caution for you is to study the implementation of an algorithm before using it in your Hadoop deployments. A non-MapReduce algorithm may not yield any speedup when run on a Hadoop cluster.

Tip

In a recent change, since April 2014, Mahout has stopped accepting...

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