Book Image

Hadoop MapReduce Cookbook

By : Srinath Perera, Thilina Gunarathne
Book Image

Hadoop MapReduce Cookbook

By: Srinath Perera, Thilina Gunarathne

Overview of this book

<p>We are facing an avalanche of data. The unstructured data we gather can contain many insights that might hold the key to business success or failure. Harnessing the ability to analyze and process this data with Hadoop MapReduce is one of the most highly sought after skills in today's job market.<br /><br />"Hadoop MapReduce Cookbook" is a one-stop guide to processing large and complex data sets using the Hadoop ecosystem. The book introduces you to simple examples and then dives deep to solve in-depth big data use cases.</p> <p>"Hadoop MapReduce Cookbook" presents more than 50 ready-to-use Hadoop MapReduce recipes in a simple and straightforward manner, with step-by-step instructions and real world examples.<br /><br />Start with how to install, then configure, extend, and administer Hadoop. Then write simple examples, learn MapReduce patterns, harness the Hadoop landscape, and finally jump to the cloud.<br /><br />The book deals with many exciting topics such as setting up Hadoop security, using MapReduce to solve analytics, classifications, on-line marketing, recommendations, and searching use cases. You will learn how to harness components from the Hadoop ecosystem including HBase, Hadoop, Pig, and Mahout, then learn how to set up cloud environments to perform Hadoop MapReduce computations.<br /><br />"Hadoop MapReduce Cookbook" teaches you how process large and complex data sets using real examples providing a comprehensive guide to get things done using Hadoop MapReduce.</p>
Table of Contents (17 chapters)
Hadoop MapReduce Cookbook
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
Index

Introduction


This chapter introduces you to several advanced Hadoop MapReduce features that will help you to develop highly customized, efficient MapReduce applications.

In this chapter, we will explore the different data types provided by Hadoop and the steps to implement custom data types for Hadoop MapReduce computations. We will also explore the different data input and output formats provided by Hadoop. This chapter will provide you with the basic understanding of how to add support for new data formats in Hadoop. We will also be discussing other advanced Hadoop features such as using DistributedCache for distribute data, using Hadoop Streaming for quick prototyping of Hadoop computations, and using Hadoop counters to report custom metrics for your computation as well as adding job dependencies to manage simple DAG-based workflows of Hadoop MapReduce computations.