Évaluation du livre
En savoir plus sur le livre
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.
Achat du livre
Guide to High Performance Distributed Computing, M. Srinivasa Sarma
- Langue
- Année de publication
- 2015
- product-detail.submit-box.info.binding
- (rigide)
Modes de paiement
Il manque plus que ton avis ici.
- Titre
- Guide to High Performance Distributed Computing
- Sous-titre
- Case Studies with Hadoop, Scalding and Spark
- Langue
- Anglais
- Auteurs
- M. Srinivasa Sarma
- Éditeur
- Springer
- Publié
- 2015
- Format
- rigide
- Pages
- 321
- ISBN10
- 3319134965
- ISBN13
- 9783319134963
- Séries
- Évaluation
- 4 sur 5
- Description
- 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.