DATA OPS
BigQuery data-freshness watcher pages when a pipeline stalls
Polls BigQuery for the most recent row timestamp across critical tables and, when a table goes stale past its SLA, pages PagerDuty and posts the lagging tables and last-seen…
How it runs
The automated pipeline, trigger to output.
- Trigger10-minute schedule fires
- ActionRead max row timestamp per critical table in BigQueryBigQuery
- LogicFlag tables past their freshness SLA
- ActionPage on-call data engineer via PagerDutyPagerDuty
- OutputPost lagging tables and last-seen times to SlackSlack
What it does
It checks the latest ingestion timestamp for each of your critical BigQuery tables on a tight schedule and compares it against each table's freshness SLA. If a table hasn't received new rows within its window, it treats that as an anomaly: it pages PagerDuty for the on-call data engineer and posts a Slack summary listing every lagging table, its last-seen time, and how far past SLA it is.
When to use it
Use it when silent pipeline stalls are your real risk, not value spikes. Dashboards that look fine but are quietly serving stale data are the classic failure this catches before stakeholders notice numbers froze.
How it works
- 1A schedule fires every 10 minutes.
- 2BigQuery returns the max row timestamp per critical table.
- 3A logic step flags any table past its freshness SLA.
- 4If any table is stale, PagerDuty is paged with the lagging set.
- 5Slack receives a summary of lagging tables and last-seen times.
Set it up
What you configure once, before turning it on.
- 1Connect BigQueryDatasets, queries, schemas.
- 2Connect PagerDutyIncidents, on-call, escalations.
- 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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Run this workflow in your colony.
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