{"product_id":"9781617298035","title":"Algorithms and Data Structures for Massive Datasets","description":"\u003ctable\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003eAuthor\/Contributor(s):\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"\"\u003eMedjedovic, Dzejla; Tahirovic, Emin; Dedovic, Ines\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003ePublisher:\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eManning\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003eDate:\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e7\/5\/2022\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003eBinding:\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"\"\u003ePaperback\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003eCondition:\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"\"\u003eNEW\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\u003cb\u003eMassive modern datasets make traditional data structures and algorithms grind to a halt. This fun and practical guide introduces cutting-edge techniques that can reliably handle even the largest distributed datasets.\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eIn \u003ci\u003eAlgorithms and Data Structures for Massive Datasets\u003c\/i\u003e you will learn:\u003cbr\u003e\u003cbr\u003eProbabilistic sketching data structures for practical problems\u003cbr\u003eChoosing the right database engine for your application\u003cbr\u003eEvaluating and designing efficient on-disk data structures and algorithms\u003cbr\u003eUnderstanding the algorithmic trade-offs involved in massive-scale systems\u003cbr\u003eDeriving basic statistics from streaming data\u003cbr\u003eCorrectly sampling streaming data\u003cbr\u003eComputing percentiles with limited space resources\u003cbr\u003e\u003cbr\u003e\u003ci\u003eAlgorithms and Data Structures for Massive Datasets\u003c\/i\u003e reveals a toolbox of new methods that are perfect for handling modern big data applications. You’ll explore the novel data structures and algorithms that underpin Google, Facebook, and other enterprise applications that work with truly massive amounts of data. These effective techniques can be applied to any discipline, from finance to text analysis. Graphics, illustrations, and hands-on industry examples make complex ideas practical to implement in your projects—and there’s no mathematical proofs to puzzle over. Work through this one-of-a-kind guide, and you’ll find the sweet spot of saving space without sacrificing your data’s accuracy.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the technology\u003c\/b\u003e\u003cbr\u003eStandard algorithms and data structures may become slow—or fail altogether—when applied to large distributed datasets. Choosing algorithms designed for big data saves time, increases accuracy, and reduces processing cost. This unique book distills cutting-edge research papers into practical techniques for sketching, streaming, and organizing massive datasets on-disk and in the cloud.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the book\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eAlgorithms and Data Structures for Massive Datasets\u003c\/i\u003e introduces processing and analytics techniques for large distributed data. Packed with industry stories and entertaining illustrations, this friendly guide makes even complex concepts easy to understand. You’ll explore real-world examples as you learn to map powerful algorithms like Bloom filters, Count-min sketch, HyperLogLog, and LSM-trees to your own use cases.\u003cbr\u003e\u003cbr\u003eWhat's inside\u003cbr\u003e\u003cbr\u003eProbabilistic sketching data structures\u003cbr\u003eChoosing the right database engine\u003cbr\u003eDesigning efficient on-disk data structures and algorithms\u003cbr\u003eAlgorithmic tradeoffs in massive-scale systems\u003cbr\u003eComputing percentiles with limited space resources\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the reader\u003c\/b\u003e\u003cbr\u003eExamples in Python, R, and pseudocode.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the author\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eDzejla Medjedovic\u003c\/b\u003e earned her PhD in the Applied Algorithms Lab at Stony Brook University, New York. \u003cb\u003eEmin Tahirovic\u003c\/b\u003e earned his PhD in biostatistics from University of Pennsylvania. Illustrator \u003cb\u003eInes Dedovic\u003c\/b\u003e earned her PhD at the Institute for Imaging and Computer Vision at RWTH Aachen University, Germany.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e1 Introduction\u003cbr\u003ePART 1 HASH-BASED SKETCHES\u003cbr\u003e2 Review of hash tables and modern hashing\u003cbr\u003e3 Approximate membership: Bloom and quotient filters\u003cbr\u003e4 Frequency estimation and count-min sketch\u003cbr\u003e5 Cardinality estimation and HyperLogLog\u003cbr\u003ePART 2 REAL-TIME ANALYTICS\u003cbr\u003e6 Streaming data: Bringing everything together\u003cbr\u003e7 Sampling from data streams\u003cbr\u003e8 Approximate quantiles on data streams\u003cbr\u003ePART 3 DATA STRUCTURES FOR DATABASES AND EXTERNAL MEMORY ALGORITHMS\u003cbr\u003e9 Introducing the external memory model\u003cbr\u003e10 Data structures for databases: B-trees, Bε-trees, and LSM-trees\u003cbr\u003e11 External memory sorting","brand":"Manning","offers":[{"title":"Default Title","offer_id":45059197403391,"sku":"9781617298035","price":59.99,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0452\/0886\/2873\/files\/Jacket_72a7a834-d659-4b5d-8252-4724d294e0d5.jpg?v=1771351513","url":"https:\/\/massivebookshop.com\/products\/9781617298035","provider":"MASSIVE BOOKSHOP","version":"1.0","type":"link"}