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Guide to high performance distribute...

Srinivasa, K.G.

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Guide to high performance distributed computing = case studies with Hadoop, Scalding and Spark /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Guide to high performance distributed computing/ by K.G. Srinivasa, Anil Kumar Muppalla.
Reminder of title:	case studies with Hadoop, Scalding and Spark /
Author:	Srinivasa, K.G.
other author:	Muppalla, Anil Kumar.
Published:	Cham :Springer International Publishing : : 2015.,
Description:	xvii, 304 p. :ill., digital ;24 cm.
[NT 15003449]:	Part I: Programming Fundamentals of High Performance Distributed Computing -- Introduction -- Getting Started with Hadoop -- Getting Started with Spark -- Programming Internals of Scalding and Spark -- Part II: Case studies using Hadoop, Scalding and Spark -- Case Study I: Data Clustering using Scalding and Spark -- Case Study II: Data Classification using Scalding and Spark -- Case Study III: Regression Analysis using Scalding and Spark -- Case Study IV: Recommender System using Scalding and Spark.
Contained By:	Springer eBooks
Subject:	High performance computing - Congresses. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-13497-0
ISBN:	9783319134970 (electronic bk.)

Guide to high performance distributed computing = case studies with Hadoop, Scalding and Spark /
Srinivasa, K.G.

Guide to high performance distributed computingcase studies with Hadoop, Scalding and Spark /[electronic resource] :by K.G. Srinivasa, Anil Kumar Muppalla. - Cham :Springer International Publishing :2015. - xvii, 304 p. :ill., digital ;24 cm. - Computer communications and networks,1617-7975. - Computer communications and networks..

Part I: Programming Fundamentals of High Performance Distributed Computing -- Introduction -- Getting Started with Hadoop -- Getting Started with Spark -- Programming Internals of Scalding and Spark -- Part II: Case studies using Hadoop, Scalding and Spark -- Case Study I: Data Clustering using Scalding and Spark -- Case Study II: Data Classification using Scalding and Spark -- Case Study III: Regression Analysis using Scalding and Spark -- Case Study IV: Recommender System using Scalding and Spark.

This timely text/reference describes the development and implementation of large-scale distributed processing systems using open source tools and technologies such as Hadoop, Scalding and Spark. Comprehensive in scope, the book presents state-of-the-art material on building high performance distributed computing systems, providing practical guidance and best practices as well as describing theoretical software frameworks. Topics and features: Describes the fundamentals of building scalable software systems for large-scale data processing in the new paradigm of high performance distributed computing Presents an overview of the Hadoop ecosystem, followed by step-by-step instruction on its installation, programming and execution Reviews the basics of Spark, including resilient distributed datasets, and examines Hadoop streaming and working with Scalding Provides detailed case studies on approaches to clustering, data classification and regression analysis Explains the process of creating a working recommender system using Scalding and Spark Supplies a complete list of supplementary source code and datasets at an associated website Fulfilling the need for both introductory material for undergraduate students of computer science and detailed discussions for software engineering professionals, this book will aid a broad audience to understand the esoteric aspects of practical high performance computing through its use of solved problems, research case studies and working source code. K.G. Srinivasa is Professor and Head of the Department of Computer Science and Engineering at M.S. Ramaiah Institute of Technology (MSRIT), Bangalore, India. His other publications include the Springer title Soft Computing for Data Mining Applications. Anil Kumar Muppalla is also a researcher at MSRIT.

ISBN: 9783319134970 (electronic bk.)

Standard No.: 10.1007/978-3-319-13497-0doiSubjects--Uniform Titles:

Apache Hadoop.
Subjects--Topical Terms:

1244541
High performance computing
--Congresses.

LC Class. No.: QA76.88

Dewey Class. No.: 004.11

Guide to high performance distributed computing = case studies with Hadoop, Scalding and Spark /
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245 1 0 $a Guide to high performance distributed computing $h [electronic resource] : $b case studies with Hadoop, Scalding and Spark / $c by K.G. Srinivasa, Anil Kumar Muppalla.
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505 0 $a Part I: Programming Fundamentals of High Performance Distributed Computing -- Introduction -- Getting Started with Hadoop -- Getting Started with Spark -- Programming Internals of Scalding and Spark -- Part II: Case studies using Hadoop, Scalding and Spark -- Case Study I: Data Clustering using Scalding and Spark -- Case Study II: Data Classification using Scalding and Spark -- Case Study III: Regression Analysis using Scalding and Spark -- Case Study IV: Recommender System using Scalding and Spark.
520 $a This timely text/reference describes the development and implementation of large-scale distributed processing systems using open source tools and technologies such as Hadoop, Scalding and Spark. Comprehensive in scope, the book presents state-of-the-art material on building high performance distributed computing systems, providing practical guidance and best practices as well as describing theoretical software frameworks. Topics and features: Describes the fundamentals of building scalable software systems for large-scale data processing in the new paradigm of high performance distributed computing Presents an overview of the Hadoop ecosystem, followed by step-by-step instruction on its installation, programming and execution Reviews the basics of Spark, including resilient distributed datasets, and examines Hadoop streaming and working with Scalding Provides detailed case studies on approaches to clustering, data classification and regression analysis Explains the process of creating a working recommender system using Scalding and Spark Supplies a complete list of supplementary source code and datasets at an associated website Fulfilling the need for both introductory material for undergraduate students of computer science and detailed discussions for software engineering professionals, this book will aid a broad audience to understand the esoteric aspects of practical high performance computing through its use of solved problems, research case studies and working source code. K.G. Srinivasa is Professor and Head of the Department of Computer Science and Engineering at M.S. Ramaiah Institute of Technology (MSRIT), Bangalore, India. His other publications include the Springer title Soft Computing for Data Mining Applications. Anil Kumar Muppalla is also a researcher at MSRIT.
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