Dockerfile is suitable for building dbt Docker images locally or using with CI/CD to automate populating a container registry.

This Dockerfile can create images for the following target: dbt-spark

In order to build a new image, run the following docker command.

docker build --tag <your_image_name> --target dbt-spark <path/to/dockerfile>

Note: Docker must be configured to use BuildKit in order for images to build properly!

By default the image will be populated with the latest version of dbt-spark on main. If you need to use a different version you can specify it by git ref using the --build-arg flag:

docker build --tag <your_image_name> \
  --target dbt-spark \
  --build-arg commit_ref=<commit_ref> \
  <path/to/dockerfile>

To build an image named "my-dbt" that supports Snowflake using the latest releases:

cd dbt-core/docker
docker build --tag my-dbt --target dbt-spark .

To build an image named "my-other-dbt" that supports Snowflake using the adapter version 1.0.0b1:

cd dbt-core/docker
docker build \
  --tag my-other-dbt \
  --target dbt-spark \
  --build-arg commit_ref=v1.0.0b1 \
 .

There are a few special cases worth noting:

• The dbt-spark database adapter comes in three different versions named PyHive, ODBC, and the default all. If you wish to override this you can use the --build-arg flag with the value of extras=<extras_name>. See the docs for more information.

docker build --tag my_dbt \
  --target dbt-spark \
  --build-arg commit_ref=v1.0.0b1 \
  --build-arg extras=PyHive \
  <path/to/dockerfile>

The ENTRYPOINT for this Dockerfile is the command dbt so you can bind-mount your project to /usr/app and use dbt as normal:

docker run \
  --network=host \
  --mount type=bind,source=path/to/project,target=/usr/app \
  --mount type=bind,source=path/to/profiles.yml,target=/root/.dbt/profiles.yml \
  my-dbt \
  ls

Notes:

• Bind-mount sources must be an absolute path
• You may need to make adjustments to the docker networking setting depending on the specifics of your data warehouse/database host.