Overview
- Provides a guide to the distributed computing technologies of Hadoop and Spark, from the perspective of industry practitioners
- Supports the theory with case studies taken from a range of disciplines, including data mining, machine learning, graph processing and image processing
- Supplies working source code to aid understanding through step-by-step implementation
- Includes supplementary material: sn.pub/extras
Part of the book series: Computer Communications and Networks (CCN)
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Table of contents (8 chapters)
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Front Matter
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Programming Fundamentals of High Performance Distributed Computing
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Front Matter
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Case Studies Using Hadoop, Scalding and Spark
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Front Matter
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Back Matter
Keywords
About this book
Authors and Affiliations
Bibliographic Information
Book Title: Guide to High Performance Distributed Computing
Book Subtitle: Case Studies with Hadoop, Scalding and Spark
Authors: K.G. Srinivasa, Anil Kumar Muppalla
Series Title: Computer Communications and Networks
DOI: https://doi.org/10.1007/978-3-319-13497-0
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer International Publishing Switzerland 2015
Hardcover ISBN: 978-3-319-13496-3Published: 09 March 2015
Softcover ISBN: 978-3-319-38347-7Published: 06 October 2016
eBook ISBN: 978-3-319-13497-0Published: 09 February 2015
Series ISSN: 1617-7975
Series E-ISSN: 2197-8433
Edition Number: 1
Number of Pages: XVII, 304
Number of Illustrations: 43 b/w illustrations
Topics: Computer Communication Networks, Programming Techniques, Data Mining and Knowledge Discovery, Artificial Intelligence, Image Processing and Computer Vision