OLAP (online analytical processing)

OLAP, online analytical processing, refers to systems and query patterns optimized for aggregating and slicing large volumes of data across multiple dimensions quickly, answering questions like revenue by region, by month, by product in a single query. It is the analytical counterpart to OLTP, online transaction processing, which is optimized for fast, individual reads and writes in operational applications.

OLAP systems are usually built on data organized with data modeling techniques such as the star schema, and historically relied on precomputed "cubes" that stored aggregations along common dimensions in advance for speed. Modern columnar data warehouses achieve similar performance by scanning and aggregating large tables efficiently at query time, without needing a separate cube layer, though the term OLAP is still used to describe this style of multi-dimensional query more broadly.

OLAP matters because it is the query pattern behind almost all reporting and business intelligence tools: filtering, grouping and drilling into a dashboard interactively is only practical when the underlying engine is built for it. A common pitfall is applying OLTP-style database design to analytical workloads, or vice versa, since the two optimize for opposite access patterns and mixing them without a proper warehouse layer leads to slow dashboards and strained operational systems.