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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.
Zakup książki
Guide to High Performance Distributed Computing, M. Srinivasa Sarma
- Język
- Rok wydania
- 2015
- Oprawa
- (twarda)
Metody płatności
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- Tytuł
- Guide to High Performance Distributed Computing
- Podtytuł
- Case Studies with Hadoop, Scalding and Spark
- Język
- angielski
- Autorzy
- M. Srinivasa Sarma
- Wydawca
- Springer
- Rok wydania
- 2015
- Oprawa
- twarda
- Liczba stron
- 321
- ISBN10
- 3319134965
- ISBN13
- 9783319134963
- Seria
- Tagi
- Ocena
- 4 z 5
- Opis
- 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.