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The Resource Guide to high performance distributed computing : case studies with Hadoop, Scalding and Spark, K.G. Srinivasa, Anil Kumar Muppalla, (electronic book)

Guide to high performance distributed computing : case studies with Hadoop, Scalding and Spark, K.G. Srinivasa, Anil Kumar Muppalla, (electronic book)

Creator
Author
Contributor
Summary
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
Language
eng
Work
Publication
Extent
1 online resource (xvii, 304 pages)
Contents
  • 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
Isbn
9783319134963
Instance
Label
Guide to high performance distributed computing : case studies with Hadoop, Scalding and Spark
Title
Guide to high performance distributed computing
Title remainder
case studies with Hadoop, Scalding and Spark
Statement of responsibility
K.G. Srinivasa, Anil Kumar Muppalla
Creator
Contributor
Author
Subject
Genre
Language
eng
Summary
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
Member of
Cataloging source
GW5XE
http://library.link/vocab/creatorName
Srinivasa, K. G
Dewey number
004.1/1
Illustrations
illustrations
Index
index present
LC call number
QA76.88
LC item number
.S75 2015eb
Literary form
non fiction
Nature of contents
  • dictionaries
  • bibliography
http://library.link/vocab/relatedWorkOrContributorName
Muppalla, Anil Kumar
Series statement
Computer Communications and Networks,
http://library.link/vocab/subjectName
  • High performance computing
  • Electronic data processing
Label
Guide to high performance distributed computing : case studies with Hadoop, Scalding and Spark, K.G. Srinivasa, Anil Kumar Muppalla, (electronic book)
Instantiates
Publication
Antecedent source
unknown
Bibliography note
Includes bibliographical references and index
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
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
Control code
SPR903687132
Dimensions
unknown
Extent
1 online resource (xvii, 304 pages)
File format
unknown
Form of item
online
Isbn
9783319134963
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other control number
10.1007/978-3-319-13497-0
Other physical details
illustrations.
Quality assurance targets
not applicable
Reformatting quality
unknown
Reproduction note
Electronic resource.
Sound
unknown sound
Specific material designation
remote
Label
Guide to high performance distributed computing : case studies with Hadoop, Scalding and Spark, K.G. Srinivasa, Anil Kumar Muppalla, (electronic book)
Publication
Antecedent source
unknown
Bibliography note
Includes bibliographical references and index
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
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
Control code
SPR903687132
Dimensions
unknown
Extent
1 online resource (xvii, 304 pages)
File format
unknown
Form of item
online
Isbn
9783319134963
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other control number
10.1007/978-3-319-13497-0
Other physical details
illustrations.
Quality assurance targets
not applicable
Reformatting quality
unknown
Reproduction note
Electronic resource.
Sound
unknown sound
Specific material designation
remote

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