Programming Elastic MapReduce

Programming Elastic MapReduce: Using AWS services to build an end-to-end application

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

Table of Contents

Chapter 1. Introduction to Amazon Elastic MapReduce
Chapter 2. Data Collection and Data Analysis with AWS
Chapter 3. Data Filtering Design Patterns and Scheduling Work
Chapter 4. Data Analysis with Hive and Pig in Amazon EMR
Chapter 5. Machine Learning Using EMR
Chapter 6. Planning AWS Projects and Managing Costs

Appendix A. Amazon Web Services Resources and Tools
Appendix B. Cloud Computing, Amazon Web Services, and Their Impacts
Appendix C. Installation and Setup