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Orchestrate your dbt™ transformation steps

Orchestrate your dbt™ transformation steps

Put your dbt transformations to work, directly from within Dagster.

About this integration

Dagster orchestrates dbt alongside other technologies, so you can schedule dbt with Spark, Python, etc. in a single data pipeline.

Dagster's Software-defined Asset approach allows Dagster to understand dbt at the level of individual dbt models. This means that you can:

  • Use Dagster's UI or APIs to run subsets of your dbt models, seeds, and snapshots.
  • Track failures, logs, and run history for individual dbt models, seeds, and snapshots.
  • Define dependencies between individual dbt models and other data assets. For example, put dbt models after the Fivetran-ingested table that they read from, or put a machine learning after the dbt models that it's trained from.


pip install dagster-dbt


from dagster_dbt import load_assets_from_dbt_project

dbt_assets = load_assets_from_dbt_project("path/to/dbt_project")

About dbt

dbt is a SQL-first transformation workflow that lets teams quickly and collaboratively deploy analytics code following software engineering best practices like modularity, portability, CI/CD, and documentation.