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Data Locality, as a book category, covers the principles, practices and implications of keeping data physically or logically close to where it is generated, processed and consumed. Titles in this genre explore technical topics—CPU/cache and memory locality, locality-aware algorithms, distributed systems, database sharding, caching strategies, edge and fog computing, and architecture patterns that reduce latency and improve throughput—as well as operational concerns like data placement, replication, synchronization, and cost/energy tradeoffs. The category also spans legal, regulatory and ethical aspects such as data residency, sovereignty, privacy, and compliance, plus applied domains like IoT, machine learning at the edge, and data-intensive scientific computing. Books range from hands-on engineering guides and case studies to theoretical treatments and policy analyses, all united by the central goal of understanding how the physical and network location of data shapes performance, reliability, cost and governance.

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