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Guide to High Performance Distributed Computing Case Studies with Hadoop, Scalding... - ebooks1001 - 11-16-2024
![[Image: 2178531e2331126788f0256e49014288.webp]](https://i124.fastpic.org/big/2024/1116/88/2178531e2331126788f0256e49014288.webp)
Free Download Guide to High Performance Distributed Computing: Case Studies with Hadoop, Scalding and Spark by K.G. Srinivasa , Anil Kumar Muppalla
English | PDF (True) | 2015 | 310 Pages | ISBN : 3319134965 | 5.6 MB
This timely text/reference describes the development and implementation of large-scale distributed processing systems using open source tools and technologies. 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. 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.
xGuide to High Performance Distributed Computing: Case Studies with Hadoop, Scalding and Spark
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Guide to High Performance Distributed Computing: Case Studies with Hadoop, Scalding and Spark by K.G. Srinivasa , Anil Kumar Muppalla
English | PDF (True) | 2015 | 310 Pages | ISBN : 3319134965 | 5.6 MB
This timely text/reference describes the development and implementation of large-scale distributed processing systems using open source tools and technologies. 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. 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.
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