Behavioral Analytics
Application Database
3rd-Party Enrichment
Incremental Engine
view: raw_users
view: dim_users
Continually updated
SQL Materialized Views
Point Look-Ups
Stream to 3rd-Party Systems

A Cloud Database Purpose-Built for
Segmentation and Personalization

We decided to look into Materialize to handle personalization and feature-serving in real-time, and by that evening we were up and running.

Tom Cooper
Tom Cooper Head of Data, Superscript

Put Analytics to Work in Customer-Facing Use Cases

Customer 360

Dynamic Pricing and Billing

App Customization and Promotions

Content and Product Recommendations

Why Materialize?

Modern Data Applications need Modern Solutions

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Traditional Warehouses: Too Slow

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Stream Processors: Too Complicated

Materialize packages the speed of stream processors in a familiar database abstraction.

Streaming EngineResultsWriteRead

Streaming Engine

Read: What is a Streaming Database?  →
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PostgreSQL Serving Layer

Read: Materialize Postgres Compatibility Explained  →

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.

Learn More

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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Streaming Inputs

Built for JOINs

Active Replication

Event-Driven Primitives

Secure and Compliant


The Warehouse-Native Approach
to Segmentation and Personalization.

“Our customers expect a personalized experience”

“Our application microservices are tough to maintain”

“Cache invalidation requires a ton of logic to get right”

“We need to join many data sources for a full customer view”

“Our segments are too large to run efficiently in real-time”

“We want to use machine learning to improve our segments”

More Use Cases

Real-Time & User-Facing Analytics

Real-Time & User-Facing Analytics

Automation and Alerting

Automation and Alerting

ML in Production

ML in Production

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