Great question — here’s the quickest, correct path to wire dbt Core into a Prefect 3.x flow and package it into your Docker image. Assumptions - You’re on Prefect 3.x with a Docker worker - You have a dbt project (dbt-core) already built - You manage prod/dev switching via

profiles.yml

+ Jinja - Credentials are stored in Prefect blocks - You’re using Postgres (adjust adapter as needed) What to import in flow.py and minimal working example - Recommended integration: PrefectDbtRunner + PrefectDbtSettings from prefect-dbt

from prefect import flow
from prefect_dbt import PrefectDbtRunner, PrefectDbtSettings

@flow(name="dbt-build")
def dbt_build_flow(
    project_dir: str = "/opt/dbt",     # path inside your container
    profiles_dir: str = "/opt/dbt",    # where profiles.yml lives
    target: str = "dev"                # or "prod"
):
    # Configure where dbt should look
    settings = PrefectDbtSettings(
        project_dir=project_dir,
        profiles_dir=profiles_dir,
        # optional: target_path=f"{project_dir}/target",
    )

    runner = PrefectDbtRunner(settings=settings)

    # Install any packages from packages.yml (safe to skip if you don't use it)
    runner.invoke(["deps"])

    # Run your dbt workflow
    # If your profiles.yml already switches targets via Jinja, you can omit --target
    runner.invoke(["build", "--target", target])

if __name__ == "__main__":
    dbt_build_flow()

Notes - If you want to pass additional dbt args, just extend the list in

invoke

, e.g.: -

runner.invoke(["run", "--select", "tag:daily"])

-

runner.invoke(["test"])

- If you prefer env vars instead of PrefectDbtSettings, you can set

DBT_PROJECT_DIR

and

DBT_PROFILES_DIR

in the environment and do

runner = PrefectDbtRunner()

. What dbt folders/files to copy into the Docker image At minimum, include your project root and these files/folders: - Required - dbt_project.yml - profiles.yml (unless you mount it separately or generate it at runtime) - models/ - Common/optional (include if you use them) - macros/ - seeds/ - tests/ - snapshots/ - analyses/ - packages.yml - Do NOT need to copy - target/ (dbt will create it) - dbt_packages/ (will be created by

dbt deps

) Example Dockerfile snippet Adapt paths to your image layout and uv/pyproject setup.

# Assuming your Python base image and uv are already set up
# Ensure dbt-core AND your adapter are installed (e.g., dbt-postgres)

# Install Prefect + prefect-dbt + dbt + adapter
# If using pyproject/uv, put these in your pyproject instead
RUN pip install prefect prefect-dbt dbt-core dbt-postgres

# Copy the dbt project into the image
# This directory should contain dbt_project.yml, profiles.yml, models/, etc.
COPY ./dbt_project /opt/dbt

# Optionally set where dbt looks for profile and project
ENV DBT_PROJECT_DIR=/opt/dbt
ENV DBT_PROFILES_DIR=/opt/dbt

# (Optional) If you want to prefetch packages at build time:
# RUN dbt deps --project-dir /opt/dbt --profiles-dir /opt/dbt

Adapter dependency - For Postgres: ensure

dbt-postgres

is installed in the image (or in your uv pyproject) - For other warehouses, install the appropriate adapter (e.g.,

dbt-snowflake

,

dbt-bigquery

) Using blocks/Jinja in profiles.yml - Keep your current Jinja-based switching for dev/prod; the flow example above will work as long as the

profiles.yml

is resolved at runtime in the container - If you render credentials from Prefect blocks inside

profiles.yml

, make sure your flow has access to the Prefect API (API URL and key) so the block lookups can succeed in the container Reference - prefect-dbt repository: prefect-dbt repo If you share: - the path inside the container where you plan to place the dbt project - which adapter you’re using (Postgres/Snowflake/BigQuery) I can tailor the code and Dockerfile exactly to your setup.