{"product_id":"mapreduce-design-patterns-9781449327170","title":"MapReduce Design Patterns","description":"\u003cp\u003eUntil 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. \u003c\/p\u003e\u003cp\u003eEach 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. \u003c\/p\u003e\u003cul\u003e \u003cli\u003e\n\u003cb\u003eSummarization patterns: \u003c\/b\u003e get a top-level view by summarizing and grouping data \u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003eFiltering patterns: \u003c\/b\u003e view data subsets such as records generated from one user \u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003eData organization patterns: \u003c\/b\u003e reorganize data to work with other systems, or to make MapReduce analysis easier \u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003eJoin patterns: \u003c\/b\u003e analyze different datasets together to discover interesting relationships \u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003eMetapatterns: \u003c\/b\u003e piece together several patterns to solve multi-stage problems, or to perform several analytics in the same job \u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003eInput and output patterns: \u003c\/b\u003e customize the way you use Hadoop to load or store data \u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003e\"A clear exposition of MapReduce programs for common data processing patterns--this book is indespensible for anyone using Hadoop.\"\u003cbr\u003e \u003cbr\u003e --Tom White, author of \u003ci\u003eHadoop: The Definitive Guide\u003c\/i\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the Author\u003c\/b\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eDonald 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.\u003c\/p\u003e\u003cp\u003eAdam 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.\u003c\/p\u003e\u003cbr\u003e","brand":"O'Reilly Media","offers":[{"title":"Default Title","offer_id":50918054822162,"sku":"9781449327170","price":43.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0831\/4771\/8930\/files\/img_638001f4-bb1b-4004-9fe7-af24dd612b69.jpg?v=1738875069","url":"https:\/\/surprise-castle.myshopify.com\/products\/mapreduce-design-patterns-9781449327170","provider":"Surprise Castle","version":"1.0","type":"link"}