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| Manning Early Access Program |
Hadoop in Practice Alex Holmes MEAP Began: December 2011 Softbound print: July 2012 (est.) | 425 pages ISBN: 9781617290237 |
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Table of Contents, MEAP Chapters & Resources
| Table of Contents | Resources |
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Part I: Background and Fundamentals
1: Getting started - FREE Part II: Data Logistics 2: Moving data in and out of Hadoop - AVAILABLE 3: Data serialization: working with text and beyond - AVAILABLE Part III: Big Data patterns 4: Applying MapReduce patterns to Big Data - AVAILABLE 5: Streamlining HDFS for Big Data - AVAILABLE 6: Diagnosing and tuning performance problems - AVAILABLE Part IV: Data science 7: Utilizing data structures and algorithms - AVAILABLE 8. Integrating R and Hadoop for statistics and more - AVAILABLE 9. Predictive analytics with Mahout Part V: Taming the elephant 10. Hacking with Hive - AVAILABLE 11. Programming Pipelines with Pig - AVAILABLE 12. Crunch and other technologies - AVAILABLE 13. Testing and debugging Appendix A: Related technologies - AVAILABLE |
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DESCRIPTION
Hadoop is a open-source platform designed to efficiently query and analyze data distributed across large clusters. It's built around MapReduce, Google's algorithm for rapidly creating a distributed index of the Internet.
Because it's especially effective for "Big Data" systems, many well-known companies use Hadoop, including Apple, eBay and LinkedIn. Yahoo and Facebook each claim to have the largest Hadoop implementation, with petabytes of data spread across thousands of machines.
The theory behind MapReduce is straight-forward: break down a large unit of work into small parts that execute in parallel across a cluster. It gets more complicated when you start applying Hadoop to problems like complex queries, statistical calculations, real-time financial transactions, and machine learning. You need tested, practical techniques you can rely on to get the job done.
Hadoop in Practice collects nearly 100 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.
WHAT'S INSIDE
- Nearly 100 tested, ready-to-use techniques
- Conceptual overview of Hadoop and MapReduce
- Real problems, real solutions
This book assumes you've already started exploring Hadoop and want concrete advice on how to use it in production.
About the Authors
Alex Holmes is a Software Engineer with over a decade of experience developing large scale distributed Java systems. He currently is a technical lead at VeriSign, using Hadoop as a Big Data platform. Alex previously developed an Internet crawl, analysis and search system using Hadoop and machine classification algorithms.
About the Early Access Version
This Early Access version of Hadoop in Practice enables you to receive new chapters as they are being written. You can also interact with the authors to ask questions, provide feedback and errata, and help shape the final manuscript on the Author Online
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