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Streaming database is a database system designed to ingest, store, and process continuous high-speed streams of data in real time, enabling incremental and low-latency analytics as new information arrives rather than only answering one-off queries over static datasets.^[1]

Although closely related to the academic concept of a data stream management system (DSMS) — which primarily focused on query semantics and execution models— a streaming database extends this idea by incorporating full database capabilities such as persistent storage and general-purpose query interfaces. Streaming databases are also distinct from stream processing engines such as Apache Flink or Storm, which perform computations on data-in-motion but rely on external storage, a streaming database integrates a storage layer with a stream processing engine. It can both store incoming stream data and continually compute query results, offering a unified system for real-time analytics.^[2] Streaming databases typically provide a SQL or SQL-like query interface so that users can define continuous queries (often as materialized views) using familiar relational syntax.^[3] Once a continuous query is registered, the system evaluates it incrementally and perpetually, emitting updated results as new data arrives, rather than only returning a snapshot as in a traditional query. ^[2]