Build data-intensive applications and services in SQL — without pipelines or caches — using materialized views that are always up-to-date.
Materialize is a fast and distributed SQL database that’s powered by streaming internals. Materialize looks like Postgres and incrementally maintains the results of SQL queries as in-memory materialized views, providing millisecond-level latency on complex transformations, joins, or aggregations.
Materialize is purpose-built for real-time data – it is a drop-in speed boost to move existing analytics pipelines and applications from batch to streaming. Developers get results that are always up-to-date and can quickly build automated, low-latency applications downstream.
Build Applications
Application developers use Materialize as a “live cache” to build and deploy data-intensive applications. Use Materialize as a pure-SQL Redis for a scalable, easily-maintained architecture.
Build with Streams
Data engineers use Materialize as a stream processor to build real-time data pipelines. Explore, query, join, and transform streams, and push data downstream to a Kafka sink.
Build Analytics
Analytics engineers use Materialize as a streaming database to go beyond dashboards to building live services. Move from batch to streaming production – all using existing SQL and tools like dbt.
Materialize uses structured data (events) as inputs. Events can come from message brokers like Kafka, change feeds of databases like PostgreSQL, or archived events from S3.
At its core, Materialize is an engine that parses SQL (joins, aggregations, transformations, computations) into dataflows, and then processes input events in order to maintain the results as an in-memory view.
Materialized views can be queried with the semantics of PostgreSQL and latency of Redis. Alternatively, updates can be streamed out, either directly into applications or as change events to Kafka.
Deep Dive into a use case:
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Josh Arenberg
Director of Engineering, DatalotStreaming data can really revolutionize this business by taking analytics that are embedded in summary tables and exposing them in a way where they can become signals that can drive other services. The vision is moving from observability of data to automation of business processes. Materialize means this can happen very quickly.
Jean-Francois Perreton
Head of Algo Quant, Kepler CheuvreuxOnce we found Materialize we were very happy because it was proper SQL and we actually are big users internally of Postgres already. One of the biggest selling points of Materialize was the fact that it offers JDBC drivers and a regular PostgreSQL facade, if you want. So it directly integrates with our third-party applications, BI tools, you name it.
Eric Dodds
Head of Growth, RudderstackAnalytics is a really obvious use-case for Materialize, but all the interesting things you can do when you enable real-time will open up a lot of creative solutions to problems that are low level plumbing problems in the stack currently – and that’s very exciting.
Tyler Richie
Co-founder and CTO, SproutfiWhat we found is that we were trying to specify data models not based on what we thought was the most correct, but what would avoid joins the most. With Materialize, this is no longer the case.
Emily Hawkins
Data Infrastructure Lead, DrizlyWorking with Materialize has been an incredibly seamless process as we can continue to write real-time SQL, exactly the same way as we already are in Snowflake with batch, so it was a much lower barrier to entry.
Ryan Gaus
Staff Software Engineer & Technical Lead, DensityWe’ve saved I-don’t-know-how-many untold quarters of trying to build our own thing.
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