
Hodnocení knihy
Více o knize
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.
Nákup knihy
Guide to High Performance Distributed Computing, M. Srinivasa Sarma
- Jazyk
- Rok vydání
- 2015
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- (pevná)
Doručení
Platební metody
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- Titul
- Guide to High Performance Distributed Computing
- Podtitul
- Case Studies with Hadoop, Scalding and Spark
- Jazyk
- anglicky
- Autoři
- M. Srinivasa Sarma
- Vydavatel
- Springer
- Rok vydání
- 2015
- Vazba
- pevná
- Počet stran
- 321
- ISBN10
- 3319134965
- ISBN13
- 9783319134963
- Série
- Hodnocení
- 4 z 5
- Anotace
- 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.