Incrementally updated materialized views - in ANSI Standard SQL and in real-time
DownloadView Source on GitHubMaterialize easily processes complex analytics over streaming datasets – accelerating development of internal tools, interactive dashboards, and customer-facing experiences. We deliver incrementally-updated materialized views in ANSI Standard SQL.
Materialize is the only technology that can enable engineers to completely develop for streaming data in a powerful declarative language – PostgreSQL – instead of building microservices.
Materialize empowers anyone within a company to discover insights from real-time data, identify problems immediately, and take action in critical moments.
Materialize connects directly to event stream processors (like Kafka) - without any preprocessing of streams required.
No "inspired by SQL" half-broken query languages. Materialize supports PostgreSQL and incrementally updates computations within milliseconds.
Use the Postgres CLI or your favorite database adapter to build interactive dashboards, internal tools, and real-time applications.
Materialize is the first and only streaming system capable of running the TPC-H decision support benchmark – a set of criteria originally developed for the world of batch data warehouses.
This standard includes support for complex joins – including complicated multiple-way joins across disparate data sources – previously unavailable in the world of streaming.
A common approach towards building streaming applications is to develop a series of custom microservices – which are often time-intensive, complicated, and ultimately brittle.
With Materialize, data engineering teams can move away from the endless development of microservices – and replace thousands of lines of Java with a few lines of SQL.
Materialize offers all of the benefits of Timely Dataflow and Differential Dataflow, accessible via the familiar and accessible PostgreSQL. Both systems have been in open-source development for over 4 years and run in production deployments at global scale.
Timely Dataflow is a low-latency cyclical dataflow computational model that excels at delivering high performance, expressive computation, and consistent results.
