MapReduce Design Patterns

Building Effective Algorithms and Analytics for Hadoop and Other Systems

Nonfiction, Computers, Advanced Computing, Programming, Data Modeling & Design
Cover of the book MapReduce Design Patterns by Donald Miner, Adam Shook, O'Reilly Media
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Author: Donald Miner, Adam Shook ISBN: 9781449341985
Publisher: O'Reilly Media Publication: November 21, 2012
Imprint: O'Reilly Media Language: English
Author: Donald Miner, Adam Shook
ISBN: 9781449341985
Publisher: O'Reilly Media
Publication: November 21, 2012
Imprint: O'Reilly Media
Language: English

Until now, design patterns for the MapReduce framework have been scattered among various research papers, blogs, and books. This handy guide brings together a unique collection of valuable MapReduce patterns that will save you time and effort regardless of the domain, language, or development framework you’re using.

Each pattern is explained in context, with pitfalls and caveats clearly identified to help you avoid common design mistakes when modeling your big data architecture. This book also provides a complete overview of MapReduce that explains its origins and implementations, and why design patterns are so important. All code examples are written for Hadoop.

  • Summarization patterns: get a top-level view by summarizing and grouping data
  • Filtering patterns: view data subsets such as records generated from one user
  • Data organization patterns: reorganize data to work with other systems, or to make MapReduce analysis easier
  • Join patterns: analyze different datasets together to discover interesting relationships
  • Metapatterns: piece together several patterns to solve multi-stage problems, or to perform several analytics in the same job
  • Input and output patterns: customize the way you use Hadoop to load or store data

"A clear exposition of MapReduce programs for common data processing patterns—this book is indespensible for anyone using Hadoop."

--Tom White, author of Hadoop: The Definitive Guide

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

Until now, design patterns for the MapReduce framework have been scattered among various research papers, blogs, and books. This handy guide brings together a unique collection of valuable MapReduce patterns that will save you time and effort regardless of the domain, language, or development framework you’re using.

Each pattern is explained in context, with pitfalls and caveats clearly identified to help you avoid common design mistakes when modeling your big data architecture. This book also provides a complete overview of MapReduce that explains its origins and implementations, and why design patterns are so important. All code examples are written for Hadoop.

"A clear exposition of MapReduce programs for common data processing patterns—this book is indespensible for anyone using Hadoop."

--Tom White, author of Hadoop: The Definitive Guide

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