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.
Uses Differential Dataflow to update query results only when input data changes, rather than recalculating the entire dataset.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
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.
Identifying suspicious transactions within milliseconds of occurrence to prevent loss.
Registry Updated:2/7/2026
Ensuring stock levels are accurate across multiple regions to prevent overselling.
Providing customers with live dashboards of their usage and billing data without lag.