DATA OPS
BigQuery Multi-Table Freshness Sweep with Morning SLA Digest
Each morning, sweeps a registry of BigQuery tables against their individual SLA windows and posts one ranked Slack digest of every stale table.
How it runs
The automated pipeline, trigger to output.
- TriggerDaily at 7am
- ActionRead table SLA registryAirtable
- ActionCheck freshness for each BigQuery tableBigQuery
- LogicRank breaches; any tier-1 stale?
- ActionPage on-call for tier-1 breachPagerDuty
- OutputPost ranked freshness digest to SlackSlack
What it does
Runs a single daily health check across many BigQuery tables, each with its own freshness window and tier. Instead of one alert per table, it produces a consolidated digest ranked by severity, and reserves paging for the highest-tier breaches.
When to use it
Use it when you own dozens of tables and want a once-a-day picture of pipeline health without alert fatigue — a standing morning report the data team reads with coffee.
How it works
- 1A schedule fires at 7am in the team's timezone.
- 2Read the table registry (table, SLA window, tier) from an Airtable base.
- 3Query BigQuery for each table's last-modified time and compute staleness against its window.
- 4Logic sorts breaches by tier and age, and flags whether any tier-1 table is stale.
- 5If a tier-1 table breached, open a PagerDuty incident for it.
- 6Post a single Slack digest listing every stale table with age, tier, and owner — green if all fresh.
Set it up
What you configure once, before turning it on.
- 1Connect BigQueryDatasets, queries, schemas.
- 2Connect AirtableBases, tables, views, automations.
- 3Connect SlackChannels, DMs, threads, mentions.
- 4Connect PagerDutyIncidents, on-call, escalations.
- 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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