000			01842nam a22002177a 4500
999			_c2450 _d2450
003			OSt
005			20200219160201.0
008			200117b ||||| |||| 00| 0 eng d
020			_a978-93-5023-981-0
028			_bAllied Informatics, Jaipur _c7084 _d13/01/2020 _q2019-20
040			_aBSDU _bEnglish _cBSDU
082			_a005.74 _bMIN
100			_aMiner, Donald
245			_aMapReduce Design Patterns
260			_aMumbai _bShroff Publishers & Distributors Pvt. Ltd. _c2019
300			_a232
500			_aUntil now, design patterns for the MapReduceframework have been scattered among various research papers, blogs, and books.This handy guide brings together a unique collection of valuable MapReducepatterns that will save you time and effort regardless of the domain, language,or development framework you’re using. Eachpattern is explained in context, with pitfalls and caveats clearly identifiedto help you avoid common design mistakes when modeling your big dataarchitecture. This book also provides a complete overview of MapReduce thatexplains its origins and implementations, and why design patterns are soimportant. Allcode 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
650			_aDesign Pattern
700			_aShook, Adam
942			_2ddc _cBK