The Cloud Database for
Fast-Changing Data.
We put a streaming engine in a database, so your team can build real-time data products without the cost, complexity, and development time of stream processing.
New Use Cases in OLAP need Correct, Up-To-Date Data.
Fresh, correct and consistent data is a prequisite to operationalizing OLAP data in your business.
Real-Time & User-Facing Analytics
Dashboards and data products need to be reactive to up-to-the-minute changes in your business.
Automation and Alerting
Save time for your users, and build value by taking action or notifying at only the right moments.
Segmentation and Personalization
Value of personalization, recommendations, dynamic pricing increases as latency of data aggregations approaches zero.
ML in Production
Online feature stores need continually updated data, operators need to monitor and react to changes in ML effectiveness.
Traditional tools don't meet the requirements.
Materialize combines the best of both worlds.
Materialize is a fast, distributed SQL database built on streaming internals.
Distributed and Cloud Native
Results are always up-to-date
Managed in standard SQL
Give your team streaming data capabilities without changing their language or workflow.
Incrementally Maintained Views
Write complex SQL transformations as materialized views that efficiently update themselves as inputs change.
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 Alerting
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 |
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 Alerting
Write alerts as SQL queries with filters and subscribe to new rows as they appear.
Learn MoreSELECT userID, email, MAX(orders.id) as last_order
FROM users
JOIN orders ON orders.userID = users.id
GROUP BY userId, email
-- Use a filter to surface users with a high % of fraud
HAVING SUM(is_fraud) / COUNT(orders.id)::FLOAT > 0.5;| userID | last_order | |
|---|---|---|
| REOtIb | a@gmail.com | 13/12/2022 |
| Y5KBE8 | b@yahoo.com | 9/12/2022 |
| Wj7JQ0 | c@hotmail.com | 13/12/2022 |
| tPCQ0 | d@xyz.com | 13/11/2022 |
Presents as PostgreSQL
Manage and query Materialize using any Postgres driver or tool.
Built for JOINs
Multi-way, complex join support across real-time streams - all in standard SQL.
Active Replication
Use replication to increase availability, reduce downtime, scale seamlessly.
Low-Latency Serving Layer
Results can be maintained in memory, making read latency similar to Redis.
Secure and Compliant
SOC 2 Type 2 compliant, encrypted at rest, secure connectivity to your infra.
Empower your Team to Build Confidently With Live Data.
Streamline solutions to hard problems.
Keep the stack and workflows you love.
Replace bespoke architectures with SQL.
Make fresh data accessible across teams.
- Shared Storage: access the same raw data, updated continually.
- Isolation of Compute: develop without resource contention.
Trusted By Data Teams
Emily Hawkins
Data Infrastructure Lead, Drizly
We can write real-time SQL, exactly the same way as we already are in Snowflake with batch.
Ryan Gaus
Staff Engineer and Tech Lead, Density
Materialize has saved us I-don’t-know-how-many untold quarters of trying to build our own thing.
Jean-Francois Perreton
Head of Algo Quant, Kepler Chevreaux
Materialize directly integrates with our third-party applications, BI tools, you name it. It’s really SQL.
Tyler Richie
Cofounder and CTO, Sproutfi
With Materialize we don't have to worry about avoiding complex joins with streaming data; we can just do them very easily.
Johan Stuyts
Data Architect, Maqqie
Materialize is correct, and not just eventually consistent. The alternatives simply don’t support consistency, and you end up wasting a lot of time troubleshooting.