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OverviewThis 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. Full Product DetailsAuthor: K.G. Srinivasa , Anil Kumar MuppallaPublisher: Springer International Publishing AG Imprint: Springer International Publishing AG Edition: Softcover reprint of the original 1st ed. 2015 Dimensions: Width: 15.50cm , Height: 1.70cm , Length: 23.50cm Weight: 0.498kg ISBN: 9783319383477ISBN 10: 3319383477 Pages: 304 Publication Date: 06 October 2016 Audience: Professional and scholarly , Professional & Vocational Format: Paperback Publisher's Status: Active Availability: Manufactured on demand  We will order this item for you from a manufactured on demand supplier. Table of ContentsPart 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.ReviewsAuthor InformationTab Content 6Author Website:Countries AvailableAll regions |
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