Name: Guide to High Performance Distributed Computing: Case Studies with Hadoop, Scalding and Spark (en Inglés)
SKU: 57474558
Price: 59450.00 CLP
Availability: InStock
Author: Srinivasa, K. G. ; Muppalla, Anil Kumar
ISBN: 9783319383477

portada Guide to High Performance Distributed Computing: Case Studies with Hadoop, Scalding and Spark (en Inglés)

Guide to High Performance Distributed Computing: Case Studies with Hadoop, Scalding and Spark (en Inglés)

Formato

Libro Físico

Autor

K. G. Srinivasa

Anil Kumar Muppalla

Editorial

Springer

Idioma

Inglés

N° páginas

304

Encuadernación

Tapa Blanda

Dimensiones

23.4x15.6x1.7 cm

Peso

0.45 kg.

ISBN13

9783319383477

N° edición

Categorías

Hardware de redes
Programación informática/desarrollo de software
Redes cliente-servidor

Guide to High Performance Distributed Computing: Case Studies with Hadoop, Scalding and Spark (en Inglés)

K. G. Srinivasa (Autor) · Anil Kumar Muppalla (Autor) · Springer · Tapa Blanda

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Reseña del libro "Guide to High Performance Distributed Computing: Case Studies with Hadoop, Scalding and Spark (en Inglés)"

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.