Materialize
Materialize is a streaming database that lets users write standard SQL, including joins and aggregations, against continuously changing data and get incrementally updated results in real time, rather than recomputing entire queries from…
Definition
Materialize is a streaming database that lets users write standard SQL, including joins and aggregations, against continuously changing data and get incrementally updated results in real time, rather than recomputing entire queries from scratch.
Overview
Materialize is built around the idea of treating SQL views as continuously maintained materialized views over streaming data. Instead of running a query once and returning a snapshot, or requiring developers to hand-write custom streaming logic in a framework like Apache Flink, Materialize lets users define a query with familiar SQL and then keeps the result incrementally up to date as new data arrives from sources such as Kafka or a database's change data capture stream. Under the hood, Materialize is built on dataflow processing techniques, specifically incremental view maintenance, so that when new input rows arrive, only the affected parts of a query's result are recomputed rather than the entire query being rerun. This makes it possible to run genuinely complex SQL — joins across multiple streams, aggregations, and subqueries — with low-latency, continuously fresh results, which is difficult to achieve efficiently with hand-rolled stream processing code. Materialize positions itself as an easier way to get real-time OLAP-style analytics without adopting the operational complexity of a full stream processing framework, aiming to let teams who already know SQL build real-time applications and dashboards on top of distributed systems and streaming data sources without learning a new programming model.
Key Features
- Standard SQL interface for defining streaming queries
- Incremental view maintenance for low-latency, always-fresh results
- Support for joins and aggregations over streaming data
- Ingestion from sources like Kafka and database change data capture
- PostgreSQL wire-protocol compatibility for existing SQL tooling
- Avoids the need to hand-write custom stream processing code