The next generation of Materialize has arrived!The next generation is here!Read the AnnouncementMore
The Streaming Database You Already Know How to Use
Materialize is a fast, distributed SQL database built on streaming internals.
PostgreSQL API
Streaming Joins
Separation of Storage & Compute
Strict Serializability
Trusted by data and engineering teams
Key Features
Take a modern cloud database, swap the query engine for a stream processor.
SQL Control Plane
Compute (Clusters)
In-Memory Indexes
Dataflows
In-Memory Indexes
Dataflows
Storage (S3)
Sources
Source Data, Tables,
Materialized Views
Materialized Views
Sinks
SQL Reads
SELECT
SUBSCRIBE
Message Brokers
Streaming Data & CDC Input Sources
Materialize continually eagerly consumes data from upstream PostgreSQL and Kafka sources.
Separate Storage & Compute
Data is durably stored in S3, while compute scales independently.
Incremental Computation Engine
Queries are parsed to dataflows, results are incrementally updated as input data changes.
Active-Active Replication
Run replicated compute clusters to increase availability and reduce downtime.
Presents as PostgreSQL
Manage and query Materialize using any Postgres driver or compatible tool.
Built for Joins on Streaming Data
Support multi-way, complex joins across real-time streams and static tables, in standard SQL.
Computation-Free Low-Latency Reads
Query results can be maintained in memory, making read latency similar to Redis.
Event-Driven Primitives
Build event-driven architectures by subscribing to changes in a query, or sink changes to Kafka.
Use Cases
What Can You Build with Materialize?
Real-Time Analytics
Use the same ANSI SQL from data warehouses to build real-time views that serve internal and customer-facing dashboards, APIs and apps.
Read MoreAutomation and Alerting
Build user-facing notifications, fraud and risk models, and automated services using event-driven SQL primitives in a streaming database.
Read MoreSegmentation and Personalization
Build engaging experiences with customer data aggregations that are always up-to-date: personalization, recommendations, dynamic pricing and more.
Read MoreML Ops
Power online feature stores with continually updated data, monitor and react to changes in ML effectiveness - all in standard SQL.
Read MoreGet the Technical 101 on Materialize
Want to learn more? We’ll send you everything you need to know, including initial concepts, getting started guides, and an overview of the internals powering a streaming database.
Why Materialize?
Joins in Materialize →
Subscribe to changes in a view →
Built from the ground up to solve the hard problems in data.
Strong Consistency Guarantees for Streaming Data
Materialize provides correct and consistent answers with minimal latency - not approximate answers or eventual consistency. With strictly-serializable computation, Materialize always delivers answers with the correct result on some specific (and recent) version of your data.
Why consistency is important in Streaming →Multi-way and Cross-Stream Joins
Whereas other systems require ahead-of-time denormalization or round-trip processing for joins, Materialize offers low-latency support for multi-way joins and complex transformations. Write the same kind of complex SQL queries you would use on a traditional data warehouse - and get real-time results.
Event-Driven Primitives: Sink and Subscribe
Because Materialize is built on streaming internals, users can stream results out of the database without performance limits using two new primitives:
- SQL clients can use
SUBSCRIBEto get pushed incremental updates to results instead of polling. - Kafka users can create
SINKSto push changes to results in a view out to a topic.
PostgreSQL Up Front, Timely Dataflow Underneath
Materialize offers an easy SQL interaction layer to a stack of powerful stream processing engines - Timely Dataflow and Differential Dataflow. Already used in correctness-critical global production deployments by Fortune 100 companies, these battle-tested systems avoid many of the shortcomings of other approaches to stream processing.
Works with Your Existing Data Stack
PostgreSQL
Streaming infrastructure is not required to use Materialize for real-time computation. Connect directly to any Postgres database via CDC and continually ingest data as it changes.
View Postgres Docsdbt
Run your existing dbt models on top of streaming data in Materialize, and dbt persists a materialized view. No matter how much or how frequently your data arrives, your model will stay up to date.
View dbt DocsKafka
Connect multiple Kafka topics to Materialize and easily explore, transform, and join streaming datasets - and sink maintained SQL query results downstream to new, enriched Kafka topics.
View Kafka DocsTrusted By Data Teams
See how Kepler uses Materialize →
Emily HawkinsData Infrastructure Lead, Drizly
We can write real-time SQL, exactly the same way as we already are in Snowflake with batch.
See how Drizly uses Materialize →Ryan GausStaff Engineer and Tech Lead, Density
Materialize has saved us I-don’t-know-how-many untold quarters of trying to build our own thing.
See how Density uses Materialize →Jean-Francois PerretonHead of Algo Quant, Kepler Chevreaux
Materialize directly integrates with our third-party applications, BI tools, you name it. It’s really SQL.
Think Declaratively, Act Incrementally
Materialize helps you get access to the power of a stream processing engine, with the simplicity of a PostgreSQL-compatible developer interface.
Fast
Millisecond-level latency through incrementally-updated materialized views.
Familiar
Control it with ANSI-standard SQL. Connect with Postgres drivers.
Fully-Featured
Support for multi-way joins, subqueries, upserts, deletes, CTEs.
Sign Up for Early Access
Register for early access to start building real-time analytics dashboards and live applications.