DATA OPS
Daily BigQuery schema-change digest with owner-routed Notion entries
Once a day, summarize every BigQuery schema change in the last 24 hours, map each to its downstream owners, and log an owner-tagged entry to a Notion review tracker.
How it runs
The automated pipeline, trigger to output.
- TriggerDaily scheduled digest run
- ActionRead 24h schema-change history from BigQueryBigQuery
- ActionResolve affected dbt models and owners from GitLabGitLab
- LogicDeduplicate and roll up changes per owner
- ActionCreate owner-tagged entries in Notion trackerNotion
- OutputPost digest summary and Notion link to SlackSlack
What it does
Produces a calm, once-daily ledger instead of real-time pings. It collects every BigQuery schema change from the past 24 hours, resolves the downstream dbt models and their owners for each change, and writes one structured entry per change into a Notion database so owners have a durable, reviewable record rather than a stream of alerts to scroll past.
When to use it
Use it when your tables change often enough that per-event alerts become noise, but you still need an auditable trail of what moved and who needs to react. Good for teams that hold a daily data standup and want a single source of truth for schema churn.
How it works
- 1A daily schedule triggers the digest run.
- 2It reads schema-change history for the window from BigQuery `INFORMATION_SCHEMA` and table metadata.
- 3For each change it parses GitLab dbt lineage to find affected models and their owners.
- 4A logic step deduplicates and rolls changes up per owner.
- 5It creates one Notion database entry per change, tagged with owner, table, and affected-model count.
- 6It posts a one-line summary with the Notion link to Slack to close the loop.
Set it up
What you configure once, before turning it on.
- 1Connect BigQueryDatasets, queries, schemas.
- 2Connect GitLabRepos, MRs, pipelines, registry.
- 3Connect NotionPages, databases, comments.
- 4Connect SlackChannels, DMs, threads, mentions.
- 5Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 6Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 7Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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