The challenge of efficiently and reliably processing huge amounts of data is addressed together by distributed computing systems and distributed databases, which are covered, respectively, in Sects. 8.1 and 8.2. Section 8.3 describes the general framework that is used in the chapter for analyzing distributed data processing, while Sect. 8.4 studies query processing in the presence of data partitioning and illustrates this in PostgreSQL. Section 8.5 introduces the notion of multidimensional tiling, a mechanism for partitioning mobility data that preserves data locality, where data located closely to each other in space and/or time are stored closely to each other on disk. Section 8.6 presents the concepts of parallel query processing, while Sect. 8.7 discusses parallelization of operations. Section 8.8 gives an introduction to Citus, an open-source extension that transforms PostgreSQL into a distributed database, exploring how it manages data distribution and decentralization. We conclude the chapter in Sects. 8.9 and 8.10 by analyzing how MobilityDB and Citus can be combined to enable a distributed mobility database using, respectively, the NYC Citi Bike dataset and the Brussels synthetic dataset generated by the BerlinMOD generator.