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The Operational Data Warehouse.

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Built to be your Trusted Operational Platform.

Materialize

Continuous computation for fresh results with strong consistency, packaged in a responsive SQL DB abstraction.

Freshness
Consistency
Responsiveness

Analytical Data Warehouse

Built for infrequent batch updates, streaming features are expensive add-ons with inadequate performance.

Freshness
Consistency
Responsiveness

Microservices + Caches

Strong consistency is impossible in bespoke services, no economies of scale, high maintenance burden.

Freshness
Consistency
Responsiveness

Stream Processors

Low-level tools that require expensive new skillsets, disruptive architectures and workflows.

Freshness
Consistency
Responsiveness

How Materialize Works

1. Start with a Cloud Data Warehouse Architecture

Storage-Compute separation delivers workload isolation and unlimited scale.

2. Add Real-Time Data Sources

Materialize has several streaming input sources that continuously pull in data from upstream OLTP Databases, Message Brokers, and other upstream systems.

3. Transform continuously with a Streaming Engine

Instead of waiting for queries and running one-shot batch transformations, data is transformed incrementally in the compute layer.

4. Add Streaming Output Options

In addition to serving standard SQL queries with high concurrency and low latency, users can subscribe to updates from a query or sink updates out for event-driven architectures.

A better way for fast-changing data

Data is queried in SQL and updated as changes happen in subsecond latency.

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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Start taking action on up-to-the-second data

By moving SQL models for fraud detection from an analytics warehouse to Materialize, Ramp cut lag from hours to seconds, stopped 60% more fraud and reduced the infra costs by 10x.

Ryan Delgado
Ryan Delgado Staff Software Engineer, Data Platform - Ramp

Key Concepts in Materialize

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