Skip to Main Content

Hadoop in Practice

Includes 85 Techniques

Summary

Hadoop in Practice collects 85 Hadoop examples and presents them in a problem/solution format. Each technique addresses a specific task you'll face, like querying big data using Pig or writing a log file loader. You'll explore each problem step by step, learning both how to build and deploy that specific solution along with the thinking that went into its design. As you work through the tasks, you'll find yourself growing more comfortable with Hadoop and at home in the world of big data.
About the Technology
Hadoop is an open source MapReduce platform designed to query and analyze data distributed across large clusters. Especially effective for big data systems, Hadoop powers mission-critical software at Apple, eBay, LinkedIn, Yahoo, and Facebook. It offers developers handy ways to store, manage, and analyze data.
About the Book
Hadoop in Practice collects 85 battle-tested examples and presents them in a problem/solution format. It balances conceptual foundations with practical recipes for key problem areas like data ingress and egress, serialization, and LZO compression. You'll explore each technique step by step, learning how to build a specific solution along with the thinking that went into it. As a bonus, the book's examples create a well-structured and understandable codebase you can tweak to meet your own needs.

This book assumes the reader knows the basics of Hadoop.

Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book.
What's Inside

  • Conceptual overview of Hadoop and MapReduce
  • 85 practical, tested techniques
  • Real problems, real solutions
  • How to integrate MapReduce and R

Table of Contents

    PART 1 BACKGROUND AND FUNDAMENTALS
  1. Hadoop in a heartbeat
  2. PART 2 DATA LOGISTICS
  3. Moving data in and out of Hadoop
  4. Data serialization?working with text and beyond
  5. PART 3 BIG DATA PATTERNS
  6. Applying MapReduce patterns to big data
  7. Streamlining HDFS for big data
  8. Diagnosing and tuning performance problems
  9. PART 4 DATA SCIENCE
  10. Utilizing data structures and algorithms
  11. Integrating R and Hadoop for statistics and more
  12. Predictive analytics with Mahout
  13. PART 5 TAMING THE ELEPHANT
  14. Hacking with Hive
  15. Programming pipelines with Pig
  16. Crunch and other technologies
  17. Testing and debugging

Alex Holmes works on tough big-data problems. He is a software engineer, author, speaker, and blogger specializing in large-scale Hadoop projects.