Programming elastic mapreduce using par schmidt kevin (10 résultats)

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paperback. Etat : Very Good. Programming Elastic MapReduce: Using Aws Services to Build an End-to-End Application This book is in very good condition and will be shipped within 24 hours of ordering. The cover may have some limited signs of wear but the pages are clean, intact and the spine remains undamaged. This book has clearly been well maintained and looked after thus far. Money back guarantee if you are not satisfied. See all our books here, order more than 1 book and get discounted shipping. .…

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Paperback or Softback. Etat : New. Programming Elastic Mapreduce: Using Aws Services to Build an End-To-End Application. Book.

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Etat : New. In English.

Paperback. Etat : Brand New. 155 pages. 9.25x7.00x0.50 inches. In Stock.

Taschenbuch. Etat : Neu. Neuware - Although you don't need a large computing infrastructure to process massive amounts of data with Apache Hadoop, it can still be difficult to get started. This practical guide shows you how to quickly launch data analysis projects in the cloud by using Amazon Elastic MapReduce (EMR), the hosted Hadoop framework in Amazon Web Services (AWS).Authors Kevin Schmidt and Christopher Phillips demonstrate best practices for using EMR and various AWS and Apache technologies by walking you through the construction of a sample MapReduce log analysis application. Using code samples and example configurations, you'll learn how to assemble the building blocks necessary to solve your biggest data analysis problems.- Get an overview of the AWS and Apache software tools used in large-scale data analysis- Go through the process of executing a Job Flow with a simple log analyzer- Discover useful MapReduce patterns for filtering and analyzing data sets- Use Apache Hive and Pig instead of Java to build a MapReduce Job Flow- Learn the basics for using Amazon EMR to run machine learning algorithms- Develop a project cost model for using Amazon EMR and other AWS tools.…

Paperback. Etat : Brand New. 155 pages. 9.25x7.00x0.50 inches. In Stock. This item is printed on demand.