Algorithms and structures for massive data sets
39.99 €
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Algorithms and structures for massive data sets
39.99 €
Standard algorithms and structures can become slow - or fail altogether - when applied to large distributed datasets. Choosing the right algorithms for big data saves time, improves accuracy, and reduces the cost of processing.
and reduces the cost of processing. This book introduces methods for processing and analyzing big distributed data. Packed with industry stories and engaging illustrations, this handy guide makes even complex concepts easy to understand. You'll learn how to apply powerful algorithms such as Bloom filters, count-min sketching, HyperLogLog, and LSM trees to your own projects using real-world examples. Examples in Python, R, and in pseudocode are provided.
Main topics:
- probabilistic data structures in outline form;
- choosing the right database engine;
- designing efficient disk-based data structures and algorithms;
- understanding algorithmic tradeoffs in large-scale systems;
- properly generating samples from streaming data;
- calculating percentiles with limited spatial resources.
and reduces the cost of processing. This book introduces methods for processing and analyzing big distributed data. Packed with industry stories and engaging illustrations, this handy guide makes even complex concepts easy to understand. You'll learn how to apply powerful algorithms such as Bloom filters, count-min sketching, HyperLogLog, and LSM trees to your own projects using real-world examples. Examples in Python, R, and in pseudocode are provided.
Main topics:
- probabilistic data structures in outline form;
- choosing the right database engine;
- designing efficient disk-based data structures and algorithms;
- understanding algorithmic tradeoffs in large-scale systems;
- properly generating samples from streaming data;
- calculating percentiles with limited spatial resources.
See also:
- All books by the publisher
- All books by the author