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LEADER 00000cam a2200745Ia 4500 
001    825076963 
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008    130124s2012    cc a    o     001 0 eng d 
019    968092087|a969060549 
020    9781449341954 
020    1449341950 
020    9781449341985 
020    1449341985 
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029 1  DEBBG|bBV041120919 
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037    CL0500000182|bSafari Books Online 
040    UMI|beng|epn|cUMI|dCOO|dDEBSZ|dOCLCQ|dXFF|dOCLCO|dOCLCQ
       |dOCLCO|dOCLCF|dOCLCO|dOCLCQ|dOCLCO|dFEM|dOCLCQ|dCEF|dUAB
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049    INap 
082 04 005.74 
082 04 005.74 
099    eBook O’Reilly for Public Libraries 
100 1  Miner, Donald. 
245 10 MapReduce design patterns /|cDonald Miner, Adam Shook.
       |h[O'Reilly electronic resource] 
260    Beijing ;|aSebastopol :|bO'Reilly,|c2012. 
300    1 online resource (xvi, 232 pages) :|billustrations 
336    text|btxt|2rdacontent 
337    computer|bc|2rdamedia 
338    online resource|bcr|2rdacarrier 
347    text file|2rda 
505 0  Design patterns and MapReduce -- Summarization patterns --
       Filtering patterns -- Data organization patterns -- Join 
       patterns -- Metapatterns -- Input and output patterns -- 
       Final thoughts and the future of design patterns. 
520    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. 
588 0  Print version record. 
590    O'Reilly|bO'Reilly Online Learning: Academic/Public 
       Library Edition 
630 00 Apache Hadoop. 
630 00 MapReduce (Computer file) 
630 07 Apache Hadoop (Computer file)|2blmlsh 
630 07 MapReduce (Computer program)|2blmlsh 
630 07 Apache Hadoop|2fast 
630 07 MapReduce (Computer file)|2fast 
650  0 Electronic data processing|xDistributed processing. 
650  0 Cluster analysis|xData processing. 
650  0 Software patterns. 
650  0 Computer algorithms. 
650  2 Algorithms 
650  6 Traitement réparti. 
650  6 Classification automatique (Statistique)|xInformatique. 
650  6 Logiciels|xModèles de conception. 
650  6 Algorithmes. 
650  7 algorithms.|2aat 
650  7 Cluster analysis|xData processing|2fast 
650  7 Computer algorithms|2fast 
650  7 Electronic data processing|xDistributed processing|2fast 
650  7 Software patterns|2fast 
700 1  Shook, Adam. 
776 08 |iPrint version:|aMiner, Donald.|tMapReduce design 
       patterns.|dSebastopol, CA : Oreilly, 2013|z9781449327170
       |w(OCoLC)792880175 
856 40 |uhttps://ezproxy.naperville-lib.org/login?url=https://
       learning.oreilly.com/library/view/~/9781449341954/?ar
       |zAvilable on O'Reilly for Public Libraries 
938    Askews and Holts Library Services|bASKH|nAH24672173 
938    Askews and Holts Library Services|bASKH|nAH24672172 
994    92|bJFN