SQL Materialized Views
A Cloud Database Purpose-Built for
Real-Time Analytics
Keep your SQL models and dbt workflow - Serve the same analytics in real-time with an efficient incremental compute engine.
“Our data team was serving real-time delivery status and location analytics to customers with two weeks of work and minimal ongoing maintenance.”
Clayton Von Hovel Data Engineer, Onward Delivery
Put Data to Work in Real-Time Analytics Use Cases
Dashboards and Business Intelligence
Materialize is PostgreSQL wire-compatible, enabling connection with BI tools like Looker, Metabase and more.
User-Facing Analytics
Query or subscribe to data in Materialize directly from customer-facing applications, without the need for complex caching.
Real-time data quality tests
Monitor your data pipelines as you would any other production system and get notified - in real-time - when data quality expectations fail. No need for an orchestrator to schedule data model runs.
Operational and IoT Reporting
Create live customer service dashboards, manage IoT device performance, and automate logistics-focused dashboards.
Modern Data Applications need Modern Solutions
Real-time analytics workloads need a different computation paradigm, but stream processing is not the answer.
Traditional Warehouses: Too Slow
Cloud Warehouses are easy to use, but get expensive when run continuously, and hit hard limits on latency and concurrency.
Stream Processors: Too Complicated
Stream Processors are fast, but they're a low-level tool, using them has high engineering costs, creates complex architectures.
Materialize packages the speed of stream processors in a familiar database abstraction.
Use the same SQL workflows from traditional warehouses but get results
updated continuously to power real-time analytics in production.
Streaming Engine
Work is done at the moment of data arrival, rather than query time, so that maintained results are available almost instantly.
PostgreSQL Serving Layer
Materialize presents as Postgres - query it with high concurrency from any tool or driver compatible with Postgres.
Managed in standard SQL
Incrementally Maintained Views
Write complex SQL transformations as materialized views that efficiently update themselves as inputs change.
Learn MoreSliding Windows
Write queries that filter to a window of time anchored to the present, Materialize will update results as time advances.
Learn MoreSQL Alerting
Write alerts as SQL queries with filters and subscribe to new rows as they appear.
Learn MoreCREATE 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 MoreCREATE 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 MoreCREATE 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 MoreSELECT 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 | last_order | |
|---|---|---|
| REOtIb | a@gmail.com | 13/12/2022 |
| Y5KBE8 | b@yahoo.com | 9/12/2022 |
| Wj7JQ0 | c@hotmail.com | 13/12/2022 |
| tPCQ0 | d@xyz.com | 13/11/2022 |
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.
Secure and Compliant
SOC 2 Type 2 compliant, encrypted at rest, secure connectivity to your infra.
The Warehouse-Native Approach
to Real-Time Analytics.
You don't need to compromise on speed, data quality, control, or simplicity.
“My warehouse is too slow - but real time is too expensive”
Get access to real time without rebuilding or rehiring: Materialize uses familiar database abstractions and ANSI-standard SQL.
“Our analytics are too join-heavy to move to real time”
Materialize efficiently handles complex SQL joins on streaming data.
“We don't want to take on the operational burden of Kafka”
No Kafka? No problem. Materialize connects directly to your Postgres DB via Change Data Capture (CDC).
“This is high-stakes data, we can't show incorrect results.”
Unlike other streaming solutions, Materialize is strongly consistent: Incomplete results are never served to the user.
“We’d like to keep our existing visualization tools”
Materialize is Postgres wire-compatible: Tools that connect to Postgres can connect and query Materialize.
“We already move Kafka data into our data warehouse”
Warehouses help you report on data, move the same SQL to Materialize to serve high concurrency, low latency data products.
More Use Cases
Automation and Alerting
→Save time for your users, and build value by taking action or notifying at only the right moments.
Real-time Fraud Detection
→Monitor transactions as they occur and stop fraud in seconds.
Segmentation and Personalization
→Value of personalization, recommendations, dynamic pricing increases as latency of data aggregations approaches zero.
ML in Production
→Online feature stores need continually updated data, operators need to monitor and react to changes in ML effectiveness.
