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.

Installation

pip install dagster-dbt

Example

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.