Programming Elastic MapReduce

by Christopher Phillips

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 analysisGo through the process of executing a Job Flow with a simple log analyzerDiscover useful MapReduce patterns for filtering and analyzing data setsUse Apache Hive and Pig instead of Java to build a MapReduce Job FlowLearn the basics for using Amazon EMR to run machine learning algorithmsDevelop a project cost model for using Amazon EMR and other AWS tools

Paperback

First published August 22, 2013

About the author

Christopher Phillips is Professor of History and Department Head at the University of Cincinnati.

Reviews

[TN · Rating 2 out of 5]

Very brief explanation about EMR. The only interesting thing was on storage strategy using reduced availability and Glacier. Of course most of the information is available in official documentation about the products.

[Frank Palardy · Rating 4 out of 5]

Dated quickly cause aws changed but well written otherwise.