Programming Elastic MapReduce

Using AWS Services to Build an End-to-End Application

By: Kevin Schmidt, Christopher Phillips

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eText | 10 December 2013 | Edition Number 1

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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