Hadoop in Practice: Includes 85 Techniques

Hadoop in Practice: Includes 85 Techniques
PDF, ePUB
  • eBook:
    Hadoop in Practice: Includes 85 Techniques
  • Author:
    Alex Holmes
  • Edition:
    1 edition
  • Categories:
  • Data:
    October 13, 2012
  • ISBN:
    1617290238
  • ISBN-13:
    9781617290237
  • Language:
    English
  • Pages:
    536 pages
  • Format:
    PDF, ePUB

Book Description
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.

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

Content

1. Background and fundamentals
Chapter 1. Hadoop in a heartbeat

2. Data logistics
Chapter 2. Moving data in and out of Hadoop
Chapter 3. Data serialization—working with text and beyond

3. Big data patterns
Chapter 4. Applying MapReduce patterns to big data
Chapter 5. Streamlining HDFS for big data
Chapter 6. Diagnosing and tuning performance problems

4. Data science
Chapter 7. Utilizing data structures and algorithms
Chapter 8. Integrating R and Hadoop for statistics and more
Chapter 9. Predictive analytics with Mahout

5. Taming the elephant
Chapter 10. Hacking with Hive
Chapter 11. Programming pipelines with Pig
Chapter 12. Crunch and other technologies
Chapter 13. Testing and debugging

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