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

What is AWS EMR?


Amazon EMR provides a hosted Hadoop, Pig, Hive, and HBase services for developers and businesses to help them build Big Data applications without worrying about the deployment complexity or managing Hadoop clusters with underlying infrastructure. Many improvements have been made into the open source Apache Hadoop and other applications in order to make them interact seamlessly with other AWS services.

Features of EMR

Let's now discuss some of the key features of EMR, most of which come with EMR being a service provided over cloud infrastructure. These are the features that are hard to achieve on an in-house local cluster:

  • Ease of use: EMR provides a hosted Hadoop service without worrying about deployment complexity or configuration challenges. We can use multiple Hadoop distributions and third-party libraries with EMR. We can easily integrate EMR with other AWS services such as S3, DynamoDB, Redshift, CloudWatch, and many more.

  • Elasticity: EMR allows you to scale up and scale...