Guide to High Performance Distributed Computing
Case Studies with Hadoop, Scalding and Spark
Published on 9. February 2015
XVII, 304 pages
978-3-319-13497-0 (ISBN)
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.
More details
Series
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Primary & secondary/elementary & high school
Illustrations
XVII, 304 p. 43 illus.
File size
5,88 MB
ISBN-13
978-3-319-13497-0 (9783319134970)
DOI
10.1007/978-3-319-13497-0
Schweitzer Classification
Other editions
Additional editions
K.G. Srinivasa | Anil Kumar Muppalla
Guide to High Performance Distributed Computing
Case Studies with Hadoop, Scalding and Spark
Book
03/2015
Springer
€53.49
Shipment within 10-15 days
Content
Part I: Programming Fundamentals of High Performance Distributed Computing.- Introduction.- Getting Started with Hadoop.- Getting Started with Spark.- Programming Internals of Scalding and Spark.- Part II: Case studies using Hadoop, Scalding and Spark.- Case Study I: Data Clustering using Scalding and Spark.- Case Study II: Data Classification using Scalding and Spark.- Case Study III: Regression Analysis using Scalding and Spark.- Case Study IV: Recommender System using Scalding and Spark.
