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

Adding streaming Job Step on EMR


Now, let's see how you can execute a streaming job on an EMR cluster. You can refer to Chapter 6, Executing Hadoop Jobs on an Amazon EMR Cluster, to launch an EMR cluster from the AWS management console and you can refer to Chapter 8, Amazon EMR – Command-line Interface Client, to launch a cluster using the CLI client tool.

Using the AWS management console

While you are launching the cluster in the Steps section, select Streaming program from the Add step drop-down selection, as shown in the following screenshot:

After that, click on Configure and add. This will bring up a pop-up box where you can define various parameters for your streaming job. You should have your mapper and reducer executables along with the input files to be present in S3. The following screenshot shows the various parameters:

After you have entered the required parameters, click on Add. Optionally, you can also enter a list of arguments (space-separated strings) to pass to the Hadoop streaming...