DATA OPS
BigQuery schema-drift blast-radius reporter for dbt models
When a tracked BigQuery table's schema changes, scan your dbt project for every model that references it and post a ranked blast-radius report to the owning teams in Slack.
How it runs
The automated pipeline, trigger to output.
- TriggerScheduled schema poll
- ActionRead current column schema from BigQuery INFORMATION_SCHEMABigQuery
- LogicDiff against last snapshot; exit if unchanged
- ActionParse dbt model lineage from GitLab repoGitLab
- LogicRank affected models by directness and column usage
- OutputPost per-team blast-radius report to SlackSlack
What it does
Watches a set of tracked BigQuery tables for schema mutations (added, dropped, or retyped columns). The moment a column the warehouse depends on changes, it walks your dbt project to find every model that selects from that table, ranks them by how directly they're affected, and warns each model's owner in Slack before broken builds reach production.
When to use it
Use it when an upstream team can repoint or retype a source column without telling analytics, and the first signal you get is a failed nightly dbt run. This turns a silent change into a targeted heads-up to exactly the people who own the affected models.
How it works
- 1A schedule wakes the workflow and it pulls current column definitions from `INFORMATION_SCHEMA` for each tracked table in BigQuery.
- 2It diffs against the last snapshot; if no columns changed it exits quietly.
- 3It reads the dbt repo from GitLab and parses `ref`/`source` lineage to find models touching the changed table.
- 4A ranking step scores each model by directness (direct source vs. transitive) and whether the specific changed column is selected.
- 5It posts a per-team blast-radius report to Slack, tagging each model's `owner` meta.
Set it up
What you configure once, before turning it on.
- 1Connect BigQueryDatasets, queries, schemas.
- 2Connect GitLabRepos, MRs, pipelines, registry.
- 3Connect SlackChannels, DMs, threads, mentions.
- 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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