Algorithms and Data Structures for Massive Datasets [Audiobook]

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 resources
Algorithms 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 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. Examples are in Python, R, and pseudocode.
About the technology
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.


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