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

PART 2 DATA LOGISTICS

PART 3 BIG DATA PATTERNS

PART 4 DATA SCIENCE

PART 5 TAMING THE ELEPHANT

1. Hadoop in a heartbeat
2. Moving data in and out of Hadoop
3. Data serialization?working with text and beyond


4. Applying MapReduce patterns to big data
5. Streamlining HDFS for big data


6. Diagnosing and tuning performance problems
7. Utilizing data structures and algorithms
8. Integrating R and Hadoop for statistics and more
9. Predictive analytics with Mahout
10. Hacking with Hive
11. Programming pipelines with Pig


12. Crunch and other technologies
13. Testing and debugging