The live data layer for apps and agents
Create real-time views into your business, just using SQL.
Trusted by engineering and data teams to deliver fresh, correct views. View Customer Stories.
Why is it so hard to keep up with demands for
real-time views into your data?
OLTP Databases
OLTP databases can’t handle data-intensive queries
Data Warehouse - OLAP
Data warehouses lack the freshness and cost-efficiency required for online services.
Do-it-yourself (DIY)
Do-it-yourself (DIY) solutions are complex to operate, and often require specialized talent.
Introducing Materialize
Create composable data products using complex transformations of live data without compromising trustworthiness.
Materialize is powered by a revolutionary engine
Built on Timely and Differential Dataflow, invented by Materialize co-founder Frank McSherry, Materialize correctly and incrementally maintains views, which can be composed into real-time data products. Scale to >1M updates per second, while maintaining freshness of single digit seconds, and query response times in single-digit milliseconds.
Work to maintain views is done continually, incrementally, and efficiently on inserts, updates, and deletes. Achieves great price performance by leveraging a mix of memory and disk.
Join fast-changing data across sources with strong consistency. Our implementation of virtual time ensures all data throughout Materialize moves from one consistent state to the next as inputs change.
"Agents and LLMs are making queries directly and accessing data with complex transformations at incredible scale. We needed something that's actually doing the work upfront."
Sam Spelberg
Co-founder, CTO
120x
reduction in page load times
60%
improvement in engineering productivity
Access via standard SQL
Incrementally Maintained Views
Write complex SQL transformations as materialized views that efficiently update themselves as inputs change.
Learn MoreBuilt for JOINs
Multi-way, complex join support, even across databases - all in standard SQL.
Learn MoreSliding Windows
Write queries that filter to a window of time anchored to the present, Materialize will update results as time advances.
Learn MoreSQL Subscriptions
Write alerts as SQL queries with filters and subscribe to new rows as they appear.
Learn MoreCREATE 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 MoreCREATE 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 MoreSELECT 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 MoreCREATE 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 MoreSUBSCRIBE (
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 | 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 |
For use cases that get better with
fresher data and more data sources.
Data-intensive UIs
Pull together data from your operational databases quickly and efficiently to improve interactivity and page-load times.
Generative AI
Deliver up-to-date context for retrieval augmented generation (RAG) pipelines and LLM-powered agents (MCP) without straining your databases.
Real-Time Process Optimization
Get fresh, consistent views of your business across data sources to build operational dashboards that help you quickly and confidently react to changes.
Automation and Alerting
Remove delays and move faster with real-time alerts and automated actions on fresh, trustworthy data.
Online Feature Store
Keep complex features up to date efficiently and use them to do things like fraud detection.
Anomaly Detection
Stream data from multiple sources in real-time and use SQL to identify patterns that deviate from normal behavior.
Three patterns for simplifying your data architecture
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.
Operational Data Store
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.
Operational Data Mesh
Use SQL to create and deliver real-time, strongly consistent data products to streamline microservice communication and coordination across domains.
Seamlessly integrate with your existing stack
Augment your data architecture to power services and business processes with fresh, correct data. Create Lambda and Kappa architectures with ease.
Loan eligibility rule calculation time decreased by 98.15%, dropping from 27 seconds to half a second.
Wolf Randall Director, Data Products, Vontive
