000			01905 a2200193 4500
020			_a9781491927922
082			_a005.74 MIN
100			_aMiner, Donald.
245			_aMapReduce design patterns
260			_bShroff Publishers and distributed Pvt. Ltd. _aMumbai _c2013
300			_a232
520			_aUntil 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
650			_aSoftware patterns
650			_aComputer algorithms
650			_aApache Hadoop
650			_aMapReduce (Computer file)
700			_aShook, Adam
942			_cBK _2ddc
999			_c44558 _d44558