Standard algorithms and structures may become slow - or not at all - when applied to large distributed datasets. The right choice of algorithms for working with big data saves time, improves accuracy and reduces processing costs. The book introduces methods for processing and analyzing large distributed data. Packed with industry stories and entertaining illustrations, this handy guide makes it easy to understand even complex concepts. You will learn how to use powerful algorithms such as Bloom filters, count-min sketch, HyperLogLog and LSM trees with real examples in your own projects. Examples are given in Python, R and pseudocode.