Book Image

Learning Big Data with Amazon Elastic MapReduce

By : Amarkant Singh, Vijay Rayapati
Book Image

Learning Big Data with Amazon Elastic MapReduce

By: Amarkant Singh, Vijay Rayapati

Overview of this book

<p>Amazon Elastic MapReduce is a web service used to process and store vast amount of data, and it is one of the largest Hadoop operators in the world. With the increase in the amount of data generated and collected by many businesses and the arrival of cost-effective cloud-based solutions for distributed computing, the feasibility to crunch large amounts of data to get deep insights within a short span of time has increased greatly.</p> <p>This book will get you started with AWS so that you can quickly create your own account and explore the services provided, many of which you might be delighted to use. This book covers the architectural details of the MapReduce framework, Apache Hadoop, various job models on EMR, how to manage clusters on EMR, and the command-line tools available with EMR. Each chapter builds on the knowledge of the previous one, leading to the final chapter where you will learn about solving a real-world use case using Apache Hadoop and EMR. This book will, therefore, get you up and running with major Big Data technologies quickly and efficiently.</p>
Table of Contents (18 chapters)
Learning Big Data with Amazon Elastic MapReduce
Credits
About the Authors
Acknowledgments
About the Reviewers
www.PacktPub.com
Preface
Index

Hello World in Hadoop


Let's quickly recap the problem we discussed in Chapter 2, MapReduce.

Note

Problem statement

Given access logs, you need to count the number of hits to your website per country. The input access logs will be in the following form:

Date, Requesting-IP-Address(remote host)

We are going to create a solution for this problem in Java to be executed over Hadoop 2.2.0. In Chapter 9, Hadoop Streaming and Advanced Hadoop Customizations, we will see how we can use Hadoop streaming to create mapper and reducer even in other languages such as Python and Ruby among others.

Development Environment Setup

We will use Hadoop 2.2.0. It requires Java 7 or later versions of Java 6 (Oracle 1.6.0_31). It is recommended that you use Java 7 (preferably Oracle Java). You can refer to http://wiki.apache.org/hadoop/HadoopJavaVersions for more information on available JREs for Hadoop.

We like to use Eclipse as our preferred IDE, you may use any other IDE as per your choice. We also recommend you to...