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Dagster & dbt

Orchestrate your dbt transformations directly with Dagster.

Dagster assets 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​

uv add dagster-dbt

Example​

from pathlib import Path

from dagster_dbt import (
    DbtCliResource,
    DbtProject,
    build_schedule_from_dbt_selection,
    dbt_assets,
)

import dagster as dg

RELATIVE_PATH_TO_MY_DBT_PROJECT = "./my_dbt_project"

my_project = DbtProject(
    project_dir=Path(__file__)
    .joinpath("..", RELATIVE_PATH_TO_MY_DBT_PROJECT)
    .resolve(),
)
my_project.prepare_if_dev()


@dbt_assets(manifest=my_project.manifest_path)
def my_dbt_assets(context: dg.AssetExecutionContext, dbt: DbtCliResource):
    yield from dbt.cli(["build"], context=context).stream()


my_schedule = build_schedule_from_dbt_selection(
    [my_dbt_assets],
    job_name="materialize_dbt_models",
    cron_schedule="0 0 * * *",
    dbt_select="fqn:*",
)

defs = dg.Definitions(
    assets=[my_dbt_assets],
    schedules=[my_schedule],
    resources={
        "dbt": DbtCliResource(project_dir=my_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.