Materialize is a reactive database that delivers incremental view updates. We help developers easily build with streaming data using standard SQL.
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SELECT
-- standard sql data types and operators
'user_' || 24::TEXT as user_id,
-- standard sql json syntax
'{"key":"value"}'::jsonb->>'key',
-- standard sql date functions
date_trunc('hour', now()),
-- standard sql aggregate functions
COUNT(t), MAX(t), AVG(t), SUM(t), STDDEV(t)
FROM my_source
GROUP BY 1,2,3;CREATE MATERIALIZED VIEW user_join AS
SELECT
u.id, SUM(p.amount), last_login
FROM users
-- Inner join
JOIN purchases p ON p.user_id=u.id
-- Left (outer) join + subquery
LEFT JOIN
SELECT user_id, MAX(ts) as last_login
FROM logins GROUP BY 1
) lg ON lg.user_id=u.id
GROUP BY u.id; 
Standard SQL
Complex Joins
Incrementally Maintained
Query
Stream
Materialize can connect to many different external sources of data without pre-processing. Connect directly to streaming sources like Kafka, Postgres databases, CDC, or historical sources of data like files or S3.
Materialize allows you to query, join, and transform data sources in standard SQL - and presents the results as incrementally-updated Materialized views. Queries are maintained and continually updated as new data streams in.
SELECT
-- standard sql data types and operators
'user_' || 24::TEXT as user_id,
-- standard sql json syntax
'{"key":"value"}'::jsonb->>'key',
-- standard sql date functions
date_trunc('hour', now()),
-- standard sql aggregate functions
COUNT(t), MAX(t), AVG(t), SUM(t), STDDEV(t)
FROM my_source
GROUP BY 1,2,3;CREATE MATERIALIZED VIEW user_join AS
SELECT
u.id, SUM(p.amount), last_login
FROM users
-- Inner join
JOIN purchases p ON p.user_id=u.id
-- Left (outer) join + subquery
LEFT JOIN
SELECT user_id, MAX(ts) as last_login
FROM logins GROUP BY 1
) lg ON lg.user_id=u.id
GROUP BY u.id; Standard SQL
Complex Joins
Incrementally Maintained
With incrementally-updated views, developers can easily build data visualizations or real-time applications. Building with streaming data can be as simple as writing a few lines of SQL.
View use cases
Query
Stream
Streaming data can really revolutionize this business by taking analytics that are embedded in summary tables and exposing them in a way where they can become signals that can drive other services. The vision is moving from observability of data to automation of business processes. Materialize means this can happen very quickly.
Once we found Materialize we were very happy because it was proper SQL and we actually are big users internally of Postgres already. One of the biggest selling points of Materialize was the fact that it offers JDBC drivers and a regular PostgreSQL facade, if you want. So it directly integrates with our third-party applications, BI tools, you name it.
Analytics is a really obvious use-case for Materialize, but all the interesting things you can do when you enable real-time will open up a lot of creative solutions to problems that are low level plumbing problems in the stack currently – and that’s very exciting. The ability to have this stuff in real-time from Materialize will be a very big driver of creativity in the way people are building experience.
Working with Materialize has been an incredibly seamless process as we can continue to write real-time SQL, exactly the same way as we already are in Snowflake with batch, so it was a much lower barrier to entry. It was also a huge plus for us that we could continue using dbt within the real-time platform to help us address our online cart conversion challenges where customers can be reminded of a pending purchase in the span of minutes versus hours.
CREATE MATERIALIZED VIEW user_join AS
SELECT
u.id, SUM(p.amount), last_login
FROM users
-- Inner join
JOIN purchases p ON p.user_id=u.id
-- Left (outer) join + subquery
LEFT JOIN (
SELECT user_id, MAX(ts) as last_login
FROM logins GROUP BY 1
) lg ON lg.user_id=u.id
GROUP BY u.id; 
SELECT
-- standard sql data types and operators
'user_' || 24::TEXT as user_id,
-- standard sql json syntax
'{"key":"value"}'::jsonb->>'key',
-- standard sql date functions
date_trunc('hour', now()),
-- standard sql aggregate functions
COUNT(t), MAX(t), AVG(t), SUM(t), STDDEV(t)
FROM my_source
GROUP BY 1,2,3;usr@vm:~$ # Step 1: Install Materialize
usr@vm:~$ apt install materialized
...
usr@vm:~$ # Step 2: Run Materialize
usr@vm:~$ materialized --workers4
...
usr@vm:~$ # Step 3: Connect to Materialize via PSQL CLI
usr@vm:~$ psql -h localhost -p 6875 -U materialize
Materialize empowers anyone within a company to discover insights from real-time data, identify problems immediately, and take action in critical moments. Build operational analytics, real-time visualizations, or connect to off-the-shelf business intelligence tools.
Once your analytics are real-time, easily build more sophisticated applications:
Build streaming applications without additional engineering resources. All you need is SQL.
Replace complex kafka-based microservices with SQL queries and reduce the cost of maintenance.
Answers are correct – not eventually consistent – without interference from late-arriving data.
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Materialize Cloud is free to try today. Register for access to start building real-time analytics dashboards and live applications.
What is Materialize?