dbt + Materialize:
Real-Time Data Superpowers

Use the best-in-class analytics engineering workflows of dbt to manage revolutionary new streaming data capabilities in Materialize.

Keep your Standard SQL

Keep the SQL modelling conventions your team is familiar with.

End Manual Orchestration

dbt run once and your views are continually kept updated.

Get Event-Driven Capabilities

Write tests that run continually on your data and alert immediately.

Featured Customer Story

"With Materialize, we can write real-time SQL, the same way as we already are in Snowflake with batch while continuing to use dbt."

Emily Hawkins
Emily Hawkins Data Infrastructure Lead, Drizly
Read Customer story
Drizly
What is dbt?

What is dbt?

dbt is a SQL-based transformation workflow that lets teams quickly and collaboratively deploy analytics code following software engineering best practices.

  • Git-enabled version control
  • Modularity
  • CI/CD
  • Documentation
  • What is Materialize?

    What is Materialize?

    Materialize is a fast, distributed, SQL database built on streaming internals.

  • Incremental Computation Engine
  • Presents as Postgres
  • Built for Joins
  • Event-Driven Primitives
  • How it works?

    Manage Materialize using dbt in Three Steps

    1
    Model

    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.

    Write SQL transformations in dbt
    2
    Run

    Use the dbt CLI to build views in Materialize

    Execute <code>dbt run</code> from the CLI to build the SQL models and transformations in Materialize once, and to migrate after updates.

    Use the dbt CLI to build views in Materialize
    3
    Materialize

    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.

    Results are automatically kept up-to-date

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