Cloud Warehouses are easy, but they get expensive when run continuously, and hit hard limits on latency and concurrency.
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.
View Customer Stories.
Fresh, correct and consistent data is a prequisite to operationalizing OLAP data in your business.
Dashboards and data products need to be reactive to up-to-the-minute changes in your business.
Save time for your users, and build value by taking action or notifying at only the right moments.
Value of personalization, recommendations, dynamic pricing increases as latency of data aggregations approaches zero.
Online feature stores need continually updated data, operators need to monitor and react to changes in ML effectiveness.
Cloud Warehouses are easy, but they get expensive when run continuously, and hit hard limits on latency and concurrency.
Stream Processors are fast, but they're a low-level tool, using them has a high-engineering cost, and results in complex architectures.
Materialize is a fast, distributed SQL database built on streaming internals.
Materialize separates storage and compute for a cloud-native architecture — and adds in a serving layer.
Read: Materialize Architecture →Work is done at the moment of data arrival, rather than query time, so that maintained results are available almost instantly.
Read: What is a Streaming Database? →Give your team streaming data capabilities without changing their language or workflow.
Write complex SQL transformations as materialized views that efficiently update themselves as inputs change.
Write queries that filter to a window of time anchored to the present, Materialize will update results as time advances.
Write alerts as SQL queries with filters and subscribe to new rows as they appear.
1CREATE MATERIALIZED VIEW my_view AS
2 SELECT userid, COUNT(api.id), COUNT(pageviews.id)
3 FROM users
4 JOIN pageviews on users.id = pageviews.userid
5 JOIN api ON users.id = api.userId
6 GROUP BY userid;
userID | api_calls | pageviews |
---|---|---|
VPLaKV | 400 | 20 |
MN37Mt | 60 | 9 |
1fT4KY | 72 | 42 |
sT4QY | 10 | 342 |
Write complex SQL transformations as materialized views that efficiently update themselves as inputs change.
Learn More1CREATE MATERIALIZED VIEW my_view AS
2 SELECT userid, COUNT(api.id), COUNT(pageviews.id)
3 FROM users
4 JOIN pageviews on users.id = pageviews.userid
5 JOIN api ON users.id = api.userId
6 GROUP BY userid;
userID | api_calls | pageviews |
---|---|---|
VPLaKV | 400 | 20 |
MN37Mt | 60 | 9 |
1fT4KY | 72 | 42 |
sT4QY | 10 | 342 |
Write queries that filter to a window of time anchored to the present, Materialize will update results as time advances.
Learn More1CREATE MATERIALIZED VIEW my_window AS
2 SELECT date_trunc('minute', received_at),
3 COUNT(*) as order_ct, SUM(amount) as revenue
4 FROM orders
5 WHERE mz_now() < received_at + interval '5 minutes'
6 GROUP BY 1;
minute | order_ct | revenue |
---|
Write alerts as SQL queries with filters and subscribe to new rows as they appear.
Learn More1SELECT userID, email, MAX(orders.id) as last_order
2FROM users
3JOIN orders ON orders.userID = users.id
4GROUP BY userId, email
5-- Use a filter to surface users with a high % of fraud
6HAVING 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 |
Manage and query Materialize using any Postgres driver or tool.
Multi-way, complex join support across real-time streams - all in standard SQL.
Use replication to increase availability, reduce downtime, scale seamlessly.
Results can be maintained in memory, making read latency similar to Redis.
SOC 2 Type 2 compliant, encrypted at rest, secure connectivity to your infra.
Materialize is built from the ground up to deliver where others have fallen short: incremental view maintenance that doesn't sacrifice consistency and correctness, and without limits on SQL complexity.
Consistency in Streaming →Get new capabilities without disruptive changes:
Save your stream processor for the most complex use cases, everything else can be SQL queries.
Run multiple workloads on the same data with ease:
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.
Ready to see if Materialize works for your use case? Register for access today!
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