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.

Trusted by data and engineering teams

Ramp Pluralsight Drizly Kepler-Chevreaux Centerfield Onward

View Customer Stories.

Why Materialize?

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

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

Automation and Alerting

Save time for your users, and build value by taking action or notifying at only the right moments.

Segmentation and Personalization

Segmentation and Personalization

Value of personalization, recommendations, dynamic pricing increases as latency of data aggregations approaches zero.

ML in Production

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.

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 combines the best of both worlds.

Materialize is a fast, distributed SQL database built on streaming internals.

Distributed and Cloud Native

Materialize separates storage and compute for a cloud-native architecture — and adds in a serving layer.
Read: Materialize Architecture  →
Streaming EngineResultsWriteRead

Results are always up-to-date

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?  →

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.

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Sliding Windows

Chevron Down

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 Alerting

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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

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 Alerting

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

Learn More
alerting.sql
SELECT 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 email last_order
REOtIb 13/12/2022
Y5KBE8 9/12/2022
Wj7JQ0 13/12/2022
tPCQ0 13/11/2022
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Presents as PostgreSQL

Manage and query Materialize using any Postgres driver or tool.

Streaming Inputs

Pull in streams of data from Kafka or stream from Postgres via replication.

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.

Event-Driven Primitives

Sink changes out to Kafka, or subscribe to query updates in standard Postgres.

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
World-Class Technology

Streamline solutions to hard problems.

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  →
works with your existing stacks, pipelines, workflows
Integrated and Compatible

Keep the stack and workflows you love.

Get new capabilities without disruptive changes:
View All Integrations  →
Standard SQL

Replace bespoke architectures with SQL.

Save your stream processor for the most complex use cases, everything else can be SQL queries.
Materialize Cloud Architecture
Cloud Architecture

Make fresh data accessible across teams.

Run multiple workloads on the same data with ease:
  • Shared Storage: access the same raw data, updated continually.
  • Isolation of Compute: develop without resource contention.
Materialize Architecture Explained  →

Trusted By Data Teams

Emily Hawkins

Emily Hawkins

Data Infrastructure Lead, Drizly

We can write real-time SQL, exactly the same way as we already are in Snowflake with batch.
See how Drizly uses Materialize  →
Ryan Gaus

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.
See how Density uses Materialize  →
Jean-Francois Perreton

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.
See how Kepler uses Materialize  →
Tyler Richie

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.
See how Sproutfi uses Materialize  →
Johan Stuyts

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.
See how Maqqie uses Materialize  →

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