MapReduce Design Patterns: Building Effective Algorithms and Analytics for Hadoop and Other Systems [NOOK Book]

Overview


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...

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MapReduce Design Patterns: Building Effective Algorithms and Analytics for Hadoop and Other Systems

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Overview


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

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Product Details

  • ISBN-13: 9781449341985
  • Publisher: O'Reilly Media, Incorporated
  • Publication date: 11/21/2012
  • Sold by: Barnes & Noble
  • Format: eBook
  • Edition number: 1
  • Pages: 252
  • Sales rank: 396,677
  • File size: 9 MB

Meet the Author

Donald Miner serves as a Solutions Architect at EMC Greenplum,
advising and helping customers implement and use Greenplum's big data systems. Prior to working with Greenplum, Dr. Miner architected several large-scale and mission-critical Hadoop deployments with the U.S. Government as a contractor. He is also involved in teaching, having previously instructed industry classes on Hadoop and a variety of artificial intelligence courses at the University of Maryland, BC. Dr. Miner received his PhD from the University of Maryland, BC in Computer Science, where he focused on Machine Learning and Multi-Agent Systems in his dissertation.

Adam Shook is a Software Engineer at ClearEdge IT Solutions, LLC,
working with a number of big data technologies such as Hadoop, Accumulo, Pig, and ZooKeeper. Shook graduated with a B.S. in Computer Science from the University of Maryland Baltimore County (UMBC) and took a job building a new high-performance graphics engine for a game studio. Seeking new challenges, he enrolled in the graduate program at UMBC with a focus on distributed computing technologies. He quickly found development work as a U.S. government contractor on a large-scale Hadoop deployment. Shook is involved in developing and instructing training curriculum for both Hadoop and Pig. He spends what little free time he has working on side projects and playing video games.

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Sort by: Showing 1 Customer Reviews
  • Posted February 10, 2013

    For Advanced Engineers Looking for More. I picked up MapReduce

    For Advanced Engineers Looking for More.

    I picked up MapReduce Design Patterns: Building Effective Algorithms and Analytics for Hadoop and Other Systems by Donald Miner and Adam Shook to explore the deeper analytics that were possible in using Hadoop and MapReduce. This book definitely did not disappoint in covering many of the more advanced challenges that engineers working with Hadoop datasets will encounter once they move from the simple into the advanced. MapReduce Design Patterns is not for the faint of heart nor the true novice in Hadoop and/or MapReduce frameworks. A solid understanding of the fundamentals of analytics is also a valuable prerequisite to this title.
    Having explored the use of Pig and Hive as a way to abstract the underlying implementations of MapReduce, MapReduce Design Patterns helped me understand what was going on “under the hood.” This was important to me as I have learned the hard lesson that sometimes the easy way is not always the most efficient and/or effective way. By reading through this title, I now better understand how I can use Pig and Hive for the straight forward analytics and MapReduce native for my more specialized needs – or in other words – use the right tool for the job.
    To the bold adventurer new to Hadoop and MapReduce – I’d suggest that you look at this book as your follow-on study guide to be used after learning the basics and working with those frameworks for a little while. In that view, I have no hesitations in recommending MapReduce Design Patterns to those engineers that are looking for something to help them move from entry level into advance levels of understanding in these technologies.

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