num_pages: 304
description: Massive 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.In Algorithms and Data Structures for Massive Datasets you will Probabilistic sketching data structures for practical problemsChoosing the right database engine for your applicationEvaluating and designing efficient on-disk data structures and algorithmsUnderstanding the algorithmic trade-offs involved in massive-scale systemsDeriving basic statistics from streaming dataCorrectly sampling streaming dataComputing percentiles with limited space resourcesAlgorithms and Data Structures for Massive Datasets 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.Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.Standard 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.Algorithms and Data Structures for Massive Datasets 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.Probabilistic sketching data structuresChoosing the right database engineDesigning efficient on-disk data structures and algorithmsAlgorithmic tradeoffs in massive-scale systemsComputing percentiles with limited space resourcesExamples in Python, R, and pseudocode.Dzejla Medjedovic earned her PhD in the Applied Algorithms Lab at Stony Brook University, New York. Emin Tahirovic earned his PhD in biostatistics from University of Pennsylvania. Illustrator Ines Dedovic earned her PhD at the Institute for Imaging and Computer Vision at RWTH Aachen University, Germany. Table of Contents1 IntroductionPART 1 HASH-BASED SKETCHES2 Review of hash tables and modern hashing3 Approximate Bloom and quotient filters4 Frequency estimation and count-min sketch5 Cardinality estimation and HyperLogLogPART 2 REAL-TIME ANALYTICS6 Streaming Bringing everything together7 Sampling from data streams8 Approximate quantiles on data streamsPART 3 DATA STRUCTURES FOR DATABASES AND EXTERNAL MEMORY ALGORITHMS9 Introducing the external memory model10 Data structures for B-trees, Bε-trees, and LSM-trees11 External memory sorting
language: English
title: Algorithms and Data Structures for Massive Datasets
publisher: Manning