In Video Editions the narrator reads the book while the content, figures, code listings, diagrams, and text appear on the screen. Like an audiobook that you can also watch as a video.
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 learn
Probabilistic sketching data structures for practical problems
Choosing the right database engine for your application
Evaluating and designing efficient on-disk data structures and algorithms
Understanding the algorithmic trade-offs involved in massive-scale systems
Deriving basic statistics from streaming data
Correctly sampling streaming data
Computing 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. 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.
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. This unique book distills cutting-edge research papers into practical techniques for sketching, streaming, and organizing massive datasets on-disk and in the cloud.
About the Book
Algorithms and Data Structures for Massive Datasets introduces processing and analytics techniques for large distributed data. Packed with industry stories, 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.
What's Inside
Probabilistic sketching data structures
Choosing the right database engine
Designing efficient on-disk data structures and algorithms
Algorithmic tradeoffs in massive-scale systems
Computing percentiles with limited space resources
About the Reader
Examples in Python, R, and pseudocode.
About the Authors
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.
Quotes
An accessible and beautifully illustrated introduction to probabilistic and disk-based data structures and algorithms.
- Marcus Young, Prosper Marketplace
Upgrade your knowledge of algorithms and data structures from textbook level to real-world level.
- Rui Liu, Oracle
Excellently explains scalable data structures and algorithms. A must-read for any data engineer.
- Alex Gout, Shopify
A detailed, practical approach to dealing with distributed system and data architectures.
- Satej Kumar Sahu, Honeywell


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