Workflow: use GitHub Copilot to accelerate Airflow DAG authoring
Data platform engineer wants a reproducible workflow for drafting Airflow DAGs and tests with Copilot assistance. Seeking prompt templates, testing steps, and repo safeguards.
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Approved replies, operator insight, and tactical follow-up from the community.
Use GitHub Copilot to draft DAG skeletons with this prompt: "Create Airflow DAG named using TaskFlow/Operator , schedule_interval , default_args, clear task_ids, docstrings, and unit-testable tasks." Testing: write pytest that imports the DAG (parse check), asserts expected task IDs/types, mocks external services, and runs task callables. Repo safeguards: branch protection + CODEOWNERS for /dags, pre-commit (black/flake8/mypy), and a CI step that parses every DAG and runs pytest.
Copilot setup & comparison: Compare GitHub Copilot and Cursor
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