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The Operational Data Warehouse.

Deliver up-to-the-second results with Materialize

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Trusted by teams that deliver fresh, correct results.

View Customer Stories.

Built to be your Trusted Operational Platform.

Delivering operational, up-to-the-second data is a challenge. Data warehouses are built to process in batches, adding complexity and cost as you move closer to real-time. Microservices are expensive and inconsistent. Stream processors are hard to manage and implement. Materialize is the only solution that packages the usability of your data warehouse with the power of streaming.

Freshness

Sub-second updates on results as data arrives.

Consistency

Strong consistency, not eventual.

Responsiveness

Consume results with the interactivity of a SQL database.

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

Why Freshness Matters →

Why Consistency Matters →

How Materialize Works

Materialize takes the familiar data warehouse architecture and makes key changes to create a uniquely powerful operational platform.

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

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

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

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;