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Fast distributed SQL query engine for big data analytics.

The streaming database for real-time analytics with incremental SQL updates.

Materialize is a revolutionary operational database designed for the 2026 data ecosystem, built on the Timely Dataflow and Differential Dataflow research. Unlike traditional databases that recompute queries from scratch, Materialize uses incremental view maintenance to update query results as new data arrives, providing sub-millisecond latency on complex JOINs and aggregations. It is fully PostgreSQL-compatible at the wire level, allowing engineers to use existing tools and libraries while shifting from batch to streaming architectures. Its 2026 market position is defined by its ability to bridge the gap between event streaming (Kafka/Pulsar) and analytical SQL, effectively replacing complex Flink jobs with standard SQL views. The architecture separates storage from compute, allowing for independent scaling and high availability. By maintaining state incrementally, Materialize eliminates the need for complex caching layers and microservices, enabling real-time feature stores, fraud detection, and dynamic pricing engines with minimal operational overhead.
Materialize is a revolutionary operational database designed for the 2026 data ecosystem, built on the Timely Dataflow and Differential Dataflow research.
Explore all tools that specialize in perform real-time analytics. This domain focus ensures Materialize delivers optimized results for this specific requirement.
Explore all tools that specialize in change data capture. This domain focus ensures Materialize delivers optimized results for this specific requirement.
Uses Differential Dataflow to update query results only when input data changes, rather than recalculating the entire dataset.
Implements the PG wire protocol and syntax, allowing use of standard PG drivers and BI tools like Metabase or Tableau.
Built-in support for time-windowing functions that automatically expire old data from memory.
Ensures strong consistency across views, meaning users never see partial or inconsistent states during updates.
Natively consumes Postgres and MySQL WAL logs without requiring an intermediary Kafka cluster.
Distributed architecture that can scale compute clusters (MZUs) independently of the underlying storage.
Ability to push results of materialized views back into Kafka or other message brokers.
Sign up for a Materialize Cloud account and create a dedicated workspace.
Define your secrets and credentials for external data sources like Kafka or Postgres.
Create a Connection object to your upstream data source (e.g., CREATE CONNECTION kafka_source).
Create a Source to ingest data into Materialize from the connection (CREATE SOURCE).
Define non-materialized Views to clean and normalize incoming data strings into structured columns.
Create Materialized Views (CREATE MATERIALIZED VIEW) to perform complex logic, JOINs, or aggregations.
Verify data freshness using standard SQL SELECT queries against the materialized views.
Configure a Sink to push results back to a downstream system like Kafka or a BI tool.
Integrate with dbt using the dbt-materialize adapter for version-controlled transformations.
Scale your cluster size (MZU) based on the memory requirements of your stateful views.
All Set
Ready to go
Verified feedback from other users.
"Users praise the Postgres compatibility and the massive reduction in infrastructure complexity compared to Flink."
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Fast distributed SQL query engine for big data analytics.

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