DATA OPS
Daily BigQuery schema change digest to Slack
Each morning, summarizes every BigQuery table schema change from the last 24 hours into a single readable Slack digest.
How it runs
The automated pipeline, trigger to output.
- TriggerMorning schedule fires
- ActionDiff BigQuery schema against yesterday's snapshotBigQuery
- LogicTag each delta additive or breaking
- ActionSummarize deltas with OpenAIOpenAI
- OutputPost daily digest to SlackSlack
- ActionSave today's snapshotPostgres
What it does
Produces one calm daily report of all schema movement in your BigQuery datasets instead of a stream of per-change alerts. It collects yesterday's column additions, removals, and type changes, uses an LLM to write a plain-English summary grouped by dataset, and posts it to Slack so the data team starts the day with full visibility.
When to use it
Use it when schema changes are frequent and mostly benign, and a noisy real-time alert per change would train people to ignore them. A once-a-day digest keeps awareness high without alert fatigue.
How it works
- 1A morning schedule trigger fires.
- 2Query BigQuery metadata for all watched datasets and diff against yesterday's stored snapshot.
- 3Collect every delta and tag each as additive or breaking.
- 4Send the structured delta list to OpenAI to compose a grouped, readable summary that calls out breaking changes first.
- 5Post the digest to Slack.
- 6Save today's snapshot for tomorrow's comparison.
Set it up
What you configure once, before turning it on.
- 1Connect BigQueryDatasets, queries, schemas.
- 2Connect OpenAIModels, embeddings, files.
- 3Connect SlackChannels, DMs, threads, mentions.
- 4Connect PostgresAny Postgres URL — query, write, migrate.
- 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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