A technical book about popular space-efficient data structures and fast algorithms that are extremely useful in modern Big Data applications.

Probabilistic data structures is a common name for data structures based mostly on different hashing techniques. Unlike regular (or deterministic) data structures, they always provide approximated answers but with reliable ways to estimate possible errors. Fortunately, the potential losses and errors are fully compensated for by extremely low memory requirements, constant query time, and scaling, the three factors that become essential in Big Data applications.

About the book

The purpose of this book is to introduce technology practitioners which includes software architects and developers, as well as technology decision makers to probabilistic data structures and algorithms.

While it is impossible to cover all the existing amazing solutions, this book is to highlight their common ideas and important areas of application, including membership querying, counting, stream mining, and similarity estimation.

This is not a book for scientists only, but to gain the most out of it you will need to have basic mathematical knowledge and an understanding of the general theory of data structures and algorithms.

What you will learn

Reading the book, you will get a theoretical and practical understanding of probabilistic data structures and learn about their common uses.

Learn how to solve practical issues of massive data handling

Master the theoretical aspects of probabilistic data structures

Identify the right data structures for your particular problems