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

Apache Hadoop MapReduce


Apache Hadoop MapReduce is the most popular implementation of the MapReduce programming paradigm. Coupled with a distributed storage framework in the form of HDFS, it provides a very robust system for processing of large datasets over a cluster of hundreds or even thousands of nodes.

The Hadoop MapReduce project can be broken down into the following three major components:

  • The MapReduce API: This includes the set of libraries available for the end users to create their applications. You will use these to create the map and reduce functions to be executed by the framework. The APIs also have provisions to set various configurations for the cluster and its components.

  • The MapReduce framework: This is the runtime implementation of various phases involved in the execution of a MapReduce task, which includes the map phase, sort/shuffle/merge phase, and the reduce phase. The intricacies of the data flow throughout various stages form the major part of this component.

  • The...