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Development of the adapter

We recommend creating a virtual environment to develop the adapter. At the time of writing, uv is a very popular tool to work with Python packages and environments. Installation should be pretty straightforward (docs).

Throughout the rest of this guide, we'll assume you're using uv. uv is a drop-in replacement for pip with greater performance and additional features. You can of course use any other tool you prefer.

Uv is super simple to use and only requires you to run a single command to do the following:

  1. Create a virtual environment
  2. Install all dependencies
  3. Install the adapter in an editable mode
  4. Install the development dependencies
uv sync

To run anything inside the virtual environment, use uv run .... Otherwise, you can activate the virtual environment before running any commands.

Testing

The integration tests require a Fabric Data Warehouse. Tell our tests how they should connect to your data warehouse by creating a file called test.env in the root of the project. You can use the provided test.env.sample as a base.

cp test.env.sample test.env

You can use the following command to run the integration tests:

uv run pytest

CI/CD

We use Docker images that have all the things we need to test the adapter in the CI/CD workflows. The Dockerfile is located in the .github directory and pushed to GitHub Packages to this repo. There is one tag per supported Python version.

All CI/CD pipelines are using GitHub Actions. The following pipelines are available:

  • publish-docker: publishes the image we use in all other pipelines.
  • integration-tests: runs the integration tests.
  • release-version: publishes the adapter to PyPI.
  • lint-format: runs ruff to check and format the code.

Releasing a new version

Ceate a git tag named v<version> and push it to GitHub. A GitHub Actions workflow will be triggered to build the package and push it to PyPI.