The Data Warehouse for Operational Workloads.

The trusted operating platform of high-impact data teams.

Ramp Pluralsight Superscript General Mills Centerfield Onward

Build data products that drive value through action.

Get operational data products into production faster with familiar SQL workflows on a real-time engine.

Real-Time & User-Facing Analytics

Real-Time & User-Facing Analytics

Automation and Alerting

Automation and Alerting

Segmentation and Personalization

Segmentation and Personalization

ML in Production

ML in Production

Traditional tools don't meet the requirements.

Materialize combines the best of both worlds.

Distributed and Cloud Native

Read: Materialize Architecture  →
Streaming EngineResultsWriteRead

Results are always up-to-date

Read: What is a Streaming Database?  →

Managed in standard SQL

Incrementally Maintained Views

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

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Sliding Windows

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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

Streaming Inputs

Built for JOINs

Active Replication

Low-Latency Serving Layer

Event-Driven Primitives

Secure and Compliant

Empower your Team to Build Confidently With Live Data.

Streamline solutions to hard problems
World-Class Technology

Streamline solutions to hard problems.

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

Keep the stack and workflows you love.

View All Integrations  →
Standard SQL

Replace bespoke architectures with SQL.

Materialize Cloud Architecture
Cloud Architecture

Make fresh data accessible across teams.

Materialize Architecture Explained  →

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