DATA OPS
BigQuery Schema-Drift Gate on dbt Pull Requests
On every pull request that touches a dbt model, compares the proposed BigQuery output schema against the live table and posts a pass/fail check plus a column-level diff comment.
How it runs
The automated pipeline, trigger to output.
- TriggerGitHub pull request touches a dbt modelGitHub
- ActionDry-compile dbt to get proposed schemaShell
- ActionFetch live table schema from BigQueryBigQuery
- LogicBranch: compatible passes, breaking continues
- OutputPost PR diff comment and set failing checkGitHub
What it does
It turns schema drift into a code-review gate instead of a production surprise. When a pull request changes a dbt model, it runs a dry compile to get the model's projected output schema, fetches the current live table schema from BigQuery, and diffs them. Backward-compatible changes get an approving check; breaking changes (removed or retyped columns that downstream consumers depend on) post a blocking review comment with the exact offending columns.
When to use it
Use it when analysts ship dbt changes through GitHub PRs and you want contract enforcement at merge time rather than after deploy. It keeps the warehouse's public-facing tables stable for the BI tools and reverse-ETL jobs that read them.
How it works
- 1A GitHub pull_request event on the dbt repo triggers the run.
- 2Run the dbt dry-compile in a shell step to produce the proposed column schema.
- 3Fetch the live table schema from BigQuery for each changed model.
- 4Branch: compatible diff posts a passing check; breaking diff continues.
- 5Post a GitHub PR review comment listing the breaking column changes and set a failing status check.
Set it up
What you configure once, before turning it on.
- 1Connect GitHubRepos, issues, pull requests, actions.
- 2Connect BigQueryDatasets, queries, schemas.
- 3Connect ShellRun sandboxed commands inside the workspace.
- 4Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 5Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 6Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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