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GoldenCheck plugs into the two places data-quality regressions usually slip through: warehouse models and pull requests.

dbt

Add the package to packages.yml:
Then run dbt deps. The package offers a SQL-native test macro and a Python runner. Use the test macro in a schema file:
Run a full scan against a model’s output:
The runner connects through profiles.yml, queries the model via dbt show, writes a temp CSV, runs the full profiler, and exits non-zero on findings at or above --fail-on.
Requires dbt-core 1.7+, goldencheck 0.5.0+, and Python 3.11+.

GitHub Actions

Scan CSV files in CI and post a findings summary on the pull request:

Inputs

Outputs

The action posts a summary table on the pull request and sets a pass/fail status check from the exit code.