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Cloud Operational Data Store.

Deliver up-to-the-second results across data sources.

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Trusted by teams that deliver fresh, correct results.

The ODS for high-performance SQL on fast-changing data.

Transforming and delivering up-to-the-second data is a challenge. Operational databases are optimized for handling transactions and struggle to manage complex queries cost-effectively. Sending operational data through a pipeline into a warehouse can introduce unacceptable end-to-end latency and substantial costs, as those systems were built to handle historical data in batches. Materialize sits alongside your operational systems and incrementally maintains strongly consistent views of your most expensive queries, allowing you to act quickly and confidently on fast-changing data.

Freshness

Sub-second updates on results as data arrives.

Consistency

Strong consistency, not eventual.

Responsiveness

Results can be consumed quickly and interactively via SQL

Materialize

Continuous computation of updates to materialized views for fresh results, and with strong consistency. Define and query views using Postgres-compatible SQL.

Freshness

Consistency

Responsiveness

Analytical Data Warehouse

Excellent performance for complex, read-heavy queries but fundamentally built for batch updates of historical data. The time between an event happening in the real world to being available in a data warehouse is often measured in minutes to hours.

Freshness

Consistency

Responsiveness

Caches

Enable very fast access to stale data. It is extremely difficult to keep these reliably up to date and consistent. Cache misses can impact database availability.

Freshness

Consistency

Responsiveness

Stream Processors

Lower-level tools that require specialized expertise. Interactive and flexible access to data joined from new sources is difficult to achieve.

Freshness

Consistency

Responsiveness

Operational Databases

Built for high performance transaction processing, but not designed for complex read queries. Materialized views and summary tables can mitigate this, but at the cost of data freshness.

Freshness

Consistency

Responsiveness

Why Freshness Matters →

Why Consistency Matters →

How Materialize Works

1. Start with Separated Storage and Compute

Storage-Compute separation delivers workload isolation and unlimited scale.

2. Add Real-Time Data Sources

Materialize has several streaming input sources that continuously pull in data from upstream OLTP Databases, streaming platforms, web hooks, and other systems.

3. Transform incrementally and query the results with SQL

Rather than running one-shot batch transformations, data to support materialized views is transformed continually in the compute layer. Instead of Extract Load and Transform, think Extract Load and Incrementally-Transform, or ELiT. The results can be queried like a standard Postgres view, just using SQL.

4. Add Streaming Output Options

In addition to serving read-intensive SQL queries with high concurrency and low latency, users can subscribe to updates from a query or sink updates out to a data warehouse or streaming platform.

A better way to join, query, and deliver fast-changing data

In Materialize, you can take data from throughout your organization and transform it into live views that can be queried and composed with strong consistency to support your business logic.

Access via standard SQL

Execute low-latency SQL queries on views via a familiar, Postgres-compatible SQL interface with support for complex joins, aggregations, and even recursion. Subscribe to queries to get notified when updates match your criteria.

Incrementally Maintained Views

Write complex SQL transformations as materialized views that efficiently update themselves as inputs change.

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Built for JOINs

Multi-way, complex join support, even across databases - all in standard SQL.

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Sliding Windows

Write queries that filter to a window of time anchored to the present, Materialize will update results as time advances.

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SQL Subscriptions

Write alerts as SQL queries with filters and subscribe to new rows as they appear.

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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

Incrementally Maintained Views

Write complex SQL transformations as materialized views that efficiently update themselves as inputs change.

Learn More

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

Built for JOINs

Multi-way, complex join support, even across databases - all in standard SQL.

Learn More

joins.sql

SELECT DISTINCT ON (auctions.id) 
   bids.amount, 
   auctions.item,
   auctions.seller
 FROM auctions, bids
 WHERE auctions.id = bids.auction_id
 ORDER BY auctions.id,
   bids.amount DESC,
   bids.buyer;

amount	item	seller

Sliding Windows

Write queries that filter to a window of time anchored to the present, Materialize will update results as time advances.

Learn More

sliding.sql

CREATE MATERIALIZED VIEW my_window AS
	SELECT date_trunc('minute', received_at),
	COUNT(*) as order_ct, SUM(amount) as revenue
	FROM orders
	WHERE mz_now() < received_at + interval '5 minutes'
	GROUP BY 1;

minute	order_ct	revenue

SQL Subscriptions

Write alerts as SQL queries with filters and subscribe to new rows as they appear.

Learn More

alerting.sql

SUBSCRIBE (
  SELECT userID, email, MAX(orders.id) AS last_order
  FROM users
  JOIN orders ON orders.userID = users.id
  GROUP BY userId, email
  HAVING SUM(is_fraud) / COUNT(orders.id)::FLOAT > 0.5
);

userID	email	last_order
REOtIb	a@gmail.com	13/12/2025
Y5KBE8	b@yahoo.com	9/12/2025
Wj7JQ0	c@hotmail.com	13/12/2025
tPCQ0	d@xyz.com	13/11/2025

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What is Materialize?

Discover how Materialize overcomes the limitations of traditional tools, delivering fresh, reliable data with SQL. Unlock query offloading, operational data meshes, and live data services for RAG architectures and dashboards.