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)
Resource Information
The item Guide to high performance distributed computing : case studies with Hadoop, Scalding and Spark, K.G. Srinivasa, Anil Kumar Muppalla, (electronic book) represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in Sydney Jones Library, University of Liverpool.This item is available to borrow from 1 library branch.
Resource Information
The item Guide to high performance distributed computing : case studies with Hadoop, Scalding and Spark, K.G. Srinivasa, Anil Kumar Muppalla, (electronic book) represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in Sydney Jones Library, University of Liverpool.
This item is available to borrow from 1 library branch.
- 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
- 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
- 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
- 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
- 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)
- 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)
- 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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<div class="citation" vocab="http://schema.org/"><i class="fa fa-external-link-square fa-fw"></i> Data from <span resource="http://link.liverpool.ac.uk/portal/Guide-to-high-performance-distributed-computing-/lfEUf4wuHx8/" typeof="Book http://bibfra.me/vocab/lite/Item"><span property="name http://bibfra.me/vocab/lite/label"><a href="http://link.liverpool.ac.uk/portal/Guide-to-high-performance-distributed-computing-/lfEUf4wuHx8/">Guide to high performance distributed computing : case studies with Hadoop, Scalding and Spark, K.G. Srinivasa, Anil Kumar Muppalla, (electronic book)</a></span> - <span property="potentialAction" typeOf="OrganizeAction"><span property="agent" typeof="LibrarySystem http://library.link/vocab/LibrarySystem" resource="http://link.liverpool.ac.uk/"><span property="name http://bibfra.me/vocab/lite/label"><a property="url" href="http://link.liverpool.ac.uk/">Sydney Jones Library, University of Liverpool</a></span></span></span></span></div>