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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.
Acquisto del libro
Guide to High Performance Distributed Computing, M. Srinivasa Sarma
- Lingua
- Pubblicato
- 2015
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- (Copertina rigida)
Metodi di pagamento
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- Titolo
- Guide to High Performance Distributed Computing
- Sottotitolo
- Case Studies with Hadoop, Scalding and Spark
- Lingua
- Inglese
- Autori
- M. Srinivasa Sarma
- Editore
- Springer
- Pubblicato
- 2015
- Formato
- Copertina rigida
- Pagine
- 321
- ISBN10
- 3319134965
- ISBN13
- 9783319134963
- Serie
- Valutazione
- 4 su 5
- Descrizione
- 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.