DATA OPS
BigQuery schema guard for dbt sources
Validates that every BigQuery source table still matches the columns your dbt sources expect, and opens a GitHub issue with a proposed fix when the schema has drifted.
How it runs
The automated pipeline, trigger to output.
- TriggerPre-build schedule fires
- ActionRead dbt sources.yml from GitHub repoGitHub
- ActionFetch actual columns from BigQuery INFORMATION_SCHEMABigQuery
- LogicCompare declared contract to actual schema
- OutputOpen GitHub issue with broken sources and yml patchGitHub
What it does
Reads the column contract declared in your dbt `sources.yml`, then checks each referenced BigQuery table to confirm those columns still exist with compatible types. When a source has drifted out from under your models, it files a GitHub issue describing exactly which source and which columns broke.
When to use it
Your dbt project depends on raw tables loaded by Fivetran, Airbyte, or a custom pipeline. You want CI-style confidence that source schemas haven't changed before you run a build that would fail mid-DAG.
How it works
- 1A scheduled trigger runs ahead of your nightly dbt build.
- 2Read the project's `sources.yml` from GitHub to get the expected columns per source.
- 3Query BigQuery's `INFORMATION_SCHEMA.COLUMNS` for each declared source table.
- 4A logic step compares declared vs. actual columns and flags missing or retyped fields.
- 5If any contract is violated, open a GitHub issue listing the source, the broken columns, and a suggested yml patch.
- 6If everything matches, exit quietly so the build proceeds.
Set it up
What you configure once, before turning it on.
- 1Connect BigQueryDatasets, queries, schemas.
- 2Connect GitHubRepos, issues, pull requests, actions.
- 3Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 4Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 5Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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