How do dbt models relate to Dagster assets?

dbt models are assets: they produce data and can have dependencies. Because of these similarities, Dagster can translate each of your dbt models into a Dagster Software-defined Asset (SDA).

How can Dagster do this? Each component of a Dagster asset has an equivalent counterpart in a dbt model:

• The asset key for a dbt model is (by default) the name of the model
• The upstream dependencies of a dbt model are defined with ref or source calls within the model's definition
• The computation required to compute the asset from its upstream dependencies is the SQL within the model's definition

From code like this:

from pathlib import Path

from dagster_dbt import DbtCliResource, dbt_assets, get_asset_key_for_model
from dagster_fivetran import build_fivetran_assets

from dagster import AssetExecutionContext, asset

fivetran_assets = build_fivetran_assets(
    connector_id="postgres",
    destination_tables=["users", "orders"],
)


@dbt_assets(manifest=Path("manifest.json"))
def dbt_project_assets(context: dg.AssetExecutionContext, dbt: DbtCliResource):
    yield from dbt.cli(["build"], context=context).stream()


@dg.asset(
    kinds={"tensorflow"},
    deps=[get_asset_key_for_model([dbt_project_assets], "daily_order_summary")],
)
def predicted_orders():
    ...

Let's break down what's happening in this example:

• Using build_fivetran_assets, we load two tables (users, orders) from a Fivetran Postgres connector as Dagster assets
• Using @dbt_assets, Dagster reads from a dbt project's manifest.json and creates Dagster assets from the dbt models it finds
• Lastly, we create a Dagster @dg.asset named predicted_orders that has an upstream dependency on a dbt asset named daily_order_summary