{"product_id":"algorithms-and-data-structures-for-massive-datasets-9781617298035","title":"Algorithms and Data Structures for Massive Datasets","description":"\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 \u003cp\u003e\u003c\/p\u003eIn \u003ci\u003eAlgorithms and Data Structures for Massive Datasets\u003c\/i\u003e you will learn: \u003cp\u003e\u003c\/p\u003eProbabilistic sketching data structures for practical problems\u003cbr\u003e Choosing the right database engine for your application\u003cbr\u003e Evaluating and designing efficient on-disk data structures and algorithms\u003cbr\u003e Understanding the algorithmic trade-offs involved in massive-scale systems\u003cbr\u003e Deriving basic statistics from streaming data\u003cbr\u003e Correctly sampling streaming data\u003cbr\u003e Computing percentiles with limited space resources \u003cp\u003e\u003c\/p\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. \u003cp\u003e\u003c\/p\u003e Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. \u003cp\u003e\u003c\/p\u003e About the technology \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. \u003cp\u003e\u003c\/p\u003e About the book \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. \u003cp\u003e\u003c\/p\u003e What's inside \u003cp\u003e\u003c\/p\u003eProbabilistic sketching data structures\u003cbr\u003e Choosing the right database engine\u003cbr\u003e Designing efficient on-disk data structures and algorithms\u003cbr\u003e Algorithmic tradeoffs in massive-scale systems\u003cbr\u003e Computing percentiles with limited space resources \u003cp\u003e\u003c\/p\u003e About the reader \u003cbr\u003eExamples in Python, R, and pseudocode. \u003cp\u003e\u003c\/p\u003e About the author \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. \u003cp\u003e\u003c\/p\u003e \u003cp\u003e\u003c\/p\u003eTable of Contents \u003cp\u003e\u003c\/p\u003e1 Introduction\u003cbr\u003e PART 1 HASH-BASED SKETCHES\u003cbr\u003e 2 Review of hash tables and modern hashing\u003cbr\u003e 3 Approximate membership: Bloom and quotient filters\u003cbr\u003e 4 Frequency estimation and count-min sketch\u003cbr\u003e 5 Cardinality estimation and HyperLogLog\u003cbr\u003e PART 2 REAL-TIME ANALYTICS\u003cbr\u003e 6 Streaming data: Bringing everything together\u003cbr\u003e 7 Sampling from data streams\u003cbr\u003e 8 Approximate quantiles on data streams\u003cbr\u003e PART 3 DATA STRUCTURES FOR DATABASES AND EXTERNAL MEMORY ALGORITHMS\u003cbr\u003e 9 Introducing the external memory model\u003cbr\u003e 10 Data structures for databases: B-trees, Bε-trees, and LSM-trees\u003cbr\u003e 11 External memory sorting\u003cbr\u003e\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 of the computer science department at Stony Brook University, NY in 2014. She has worked on a number of projects in algorithms for massive data, taught algorithms at various levels and also spent some time at Microsoft. \u003cp\u003e\u003c\/p\u003e\u003cb\u003eEmin Tahirovic\u003c\/b\u003e earned his doctorate in biostatistics from UPenn in 2016, and his master's degree in theoretical computer science from Goethe University in Frankfurt in 2008. He has worked for DBahn AG as an IT consultant and he regularly consults on projects for pharma and tech companies. \u003cp\u003e\u003c\/p\u003e\u003cb\u003eInes Dedovic\u003c\/b\u003e earned her PhD at the Institute for Imaging and Computer Vision of the Department of Electrical Engineering at RWTH Aachen University, Germany. She has worked as a researcher at the Research Center J?lich and is currently employed as a software developer for camera systems at Jonas \u0026amp; Redmann, an automation company.\u003cbr\u003e","brand":"Manning Publications","offers":[{"title":"Default Title","offer_id":50900930265362,"sku":"9781617298035","price":55.99,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0831\/4771\/8930\/files\/img_87903ce9-6782-4f66-a38a-7fae1488458f.jpg?v=1738405792","url":"https:\/\/surprise-castle.myshopify.com\/products\/algorithms-and-data-structures-for-massive-datasets-9781617298035","provider":"Surprise Castle","version":"1.0","type":"link"}