Hadoop MapReduce v2 Cookbook, 2nd Edition Front Cover

Hadoop MapReduce v2 Cookbook, 2nd Edition

  • Length: 293 pages
  • Edition: 2
  • Publisher:
  • Publication Date: 2015-01-25
  • ISBN-10: 1783285478
  • ISBN-13: 9781783285471
  • Sales Rank: #2856416 (See Top 100 Books)

Explore the Hadoop MapReduce v2 ecosystem to gain insights from very large datasets

About This Book

  • Process large and complex datasets using next generation Hadoop
  • Install, configure, and administer MapReduce programs and learn what’s new in MapReduce v2
  • More than 90 Hadoop MapReduce recipes presented in a simple and straightforward manner, with step-by-step instructions and real-world examples

Who This Book Is For

If you are a Big Data enthusiast and wish to use Hadoop v2 to solve your problems, then this book is for you. This book is for Java programmers with little to moderate knowledge of Hadoop MapReduce. This is also a one-stop reference for developers and system admins who want to quickly get up to speed with using Hadoop v2. It would be helpful to have a basic knowledge of software development using Java and a basic working knowledge of Linux.

In Detail

Starting with installing Hadoop YARN, MapReduce, HDFS, and other Hadoop ecosystem components, with this book, you will soon learn about many exciting topics such as MapReduce patterns, using Hadoop to solve analytics, classifications, online marketing, recommendations, and data indexing and searching. You will learn how to take advantage of Hadoop ecosystem projects including Hive, HBase, Pig, Mahout, Nutch, and Giraph and be introduced to deploying in cloud environments.

Finally, you will be able to apply the knowledge you have gained to your own real-world scenarios to achieve the best-possible results.

Table of Contents

Chapter 1. Getting Started with Hadoop v2
Chapter 2. Cloud Deployments – Using Hadoop YARN on Cloud Environments
Chapter 3. Hadoop Essentials – Configurations, Unit Tests, and Other APIs
Chapter 4. Developing Complex Hadoop MapReduce Applications
Chapter 5. Analytics
Chapter 6. Hadoop Ecosystem – Apache Hive
Chapter 7. Hadoop Ecosystem II – Pig, HBase, Mahout, and Sqoop
Chapter 8. Searching and Indexing
Chapter 9. Classifications, Recommendations, and Finding Relationships
Chapter 10. Mass Text Data Processing

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