Databases are built to answer any query.
But we keep asking them the same things.
Databases and warehouses can answer any SQL query you give them, but we often just need the answer to a few queries over and over again.
Traditional Approach
Teams are forced to choose from one of several compromises:
- Query the DB: Doesn't scale, exposure to overload.
- Cache: Use stale data, deal with cache invalidation.
- Denormalize: Maintain complex update logic in code.
- Stream: Hire a team to handle expensive stream processing.
Materialize Approach
Materialize efficiently incrementally maintains complex transformations defined in standard SQL.
- ANSI SQL: No new languages or advanced concepts
- Results are always fresh
- Queries are fast and concurrent
- Connected to a Mature Ecosystem
How it works?
Manage Materialize using dbt in Three Steps
Write SQL transformations in dbt
Maintain the entire schema of your SQL transformations (sources, views, materialized views) in a dbt project in a git repo.
Use the dbt CLI to build views in Materialize
Execute `dbt run` from the CLI to build the SQL models and transformations in Materialize once, and to migrate after updates.
Results are automatically kept up-to-date
Materialize automatically updates the results of SQL transformations as source data changes. Results can be queried in SQL or streamed out.
Sign Up for Early Access
Register for early access to start building real-time analytics dashboards and live applications.
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