A data processing approach that handles large volumes of data operations by employing clusters of comparatively low-specification servers. To handle more data, it's as easy as adding more machines to the cluster. Compared to vertical scaling which runs on a single machine that handles larger volumes of operations by upgrading with a faster processor or more memory, the horizontal scaling approach tends to be cheaper as the number of operations and the size of the data increases. Many recent and very largest data processing pipelines are all built on a horizontal scaling model.

There is a cost to the horizontal approach as writing distributed data handling code is tricky and involves tradeoffs between speed, scalability, fault tolerance, and traditional database goals like atomicity and consistency. There a many database systems that have been designed using the horizontal model such as Hadoop and Cassandra.