The live data layer for apps and AI agents

Create up-to-the-second views into your business, just using SQL.

How Materialize Works

1. Integrate Data from Disparate Systems

Point Materialize at your operational databases, event/queue systems, and services. Sources will appear as continually updating tables in Materialize with sub-second freshness.

2. Define views in SQL

Create views that join, aggregate, and shape raw tables into canonical business objects. A common pattern is a medallion architecture that translates source tables from bronze (raw) to silver (intermediate) to gold (final) data products.

3. Index views to create data flows

In a traditional database, you would index a table to speed up queries. In Materialize, you index the views themselves. When you do the Materialize optimizer creates dataflows, or computational graphs, that do the minimum amount of work as writes come in to keep views correctly and continually up to date. Now reactive, or pull based computation, becomes proactive and push based.

4. Strong consistency across the entire system

Materializes uses virtual time to ensure all objects are coordinated from a single clock. The system moves from one consistent state to another, reflecting things like transaction boundaries in up stream systems, even when views that are hundreds of layers deep.

5. Agentic feedback loops are now possible

Expose "gold" data products via MCP to give agents access to a semantic representation of your business. They can take the results of data products as-is, or use SQL to apply additional transformations on-the-fly. This lets them observe the world, think, and take action. Because Materialize can show how the results of those actions impact any other view in the system with sub-second latency, agents can now see the results of their own actions, quickly course correct, and take further actions to proceed toward their goal.

6. Make live data available across your entire stack

Push all transformed updates to downstream system like Kafka and Apache Iceberg or subscribe to an interesting subset. You can also take a pull-based approach and query data from Materialize using standard Postgres drivers. While Materialize doesn't have the fastest query engine, because it can do so much work up front, the end-to-end latency for reflecting the result of updates is usually far better than even OLAP systems.

Getting your data AI-agent ready

Provide fresh contextual building blocks

Published views become live data products, canonical business objects that agents can discover, compose, and reuse. Each expands agent capabilities without extra engineering. Materialize keeps them updated in under a second, giving agents a live digital twin to act and see impact, powering autonomous and human-in-the-loop workflows.

Keep vector embeddings and attributes fresh

Enable relevant semantic search and re-ranking in production by quickly calculating attributes to store with vectors as source systems update. Minimize embedding cost by avoiding batch refreshes; instead use live views to update only required objects. Avoid write-heavy attribute workloads by joining results with pre-computed results to quickly enrich objects returned from search.

Reduce token and compute costs

Because Materialize incrementally maintains data products, agents don’t need to spend tokens reason over low-level data models. Serving higher-level, contextual building blocks lowers infrastructure load and allows agents to spend tokens on goal seeking rather than data manipulation.

Transform your data with Postgres-compatible SQL.

Perform multi-way, lateral, and outer joins across your connected data sources. Materialize maintains even the most complex join graphs incrementally, so your unified view is always fresh.

incremental.sql

CREATE MATERIALIZED VIEW my_view AS
	SELECT userid, COUNT(api.id), COUNT(pageviews.id)
	FROM users
	JOIN pageviews on users.id = pageviews.userid
	JOIN api ON users.id = api.userId
	GROUP BY userid;

userID	api_calls	pageviews
VPLaKV	400	20
MN37Mt	60	9
1fT4KY	72	42
sT4QY	10	342

Looking for strategies, tips, or customer stories?

All-in-one Docker image. The fastest way to get hands-on experience with Materialize for local development.

Browse Resources Learn more

Elastic. Efficient. Secure.

Materialize isolates workloads to protect latency-sensitive paths, keeps hardware costs under control, and integrates seamlessly with your security model.

Elastic Compute

Scale reads independently; isolate workloads to protect latency‑sensitive paths.

Beyond In-Memory

Use Materialize for views that are too large to fit in memory. Materialize automatically spills to disk to improve economics on your largest operational workloads.

Secure by Design

Materialize integrates with your existing controls, from AWS PrivateLink to role-based access control, so security is baked in from day one across cloud and on-prem deployments.

Implementation Approaches

Patterns to Simplify your Architecture

Operational Data Mesh

Use SQL to create and deliver real-time, strongly consistent data products to streamline microservice communication and provide fresh-context to agents.

Operational Data Layer

Extract, load, and incrementally transform data from multiple sources. Create live views of your data that can be queried directly or loaded into systems like data warehouses or stream processing platforms.

Query offload

Scale complex read queries more efficiently than a read replica, and without the invalidation headaches of a cache. Simply send updates to Materialize, create materialized views using SQL, and directly query the correct, incrementally maintained results.

Data Integration

Sources and Destinations

In: operational databases and event/queue systems across your estate
Out: low‑latency SQL for apps and APIs; changefeeds/sinks to warehouses, queues, caches, and vector databases.
Many‑to‑many. Support for multiple sources and multiple destinations so teams can assemble once and reuse everywhere.