DATA OPS
dbt Model Freshness SLA Breach Alert to Slack
Checks the last-built timestamp of critical BigQuery dbt models on a schedule and posts a Slack alert the moment any model exceeds its freshness SLA.
How it runs
The automated pipeline, trigger to output.
- TriggerEvery 15 minutes (schedule)
- ActionQuery last-built timestamp per watched modelBigQuery
- LogicCompare model age to per-model SLA, keep breaches
- LogicExit quietly if no models are over SLA
- OutputPost breach summary to data-ops Slack channelSlack
What it does
Monitors the freshness of your most important BigQuery dbt models against per-model SLA windows and raises a Slack alert the instant a model goes stale, so analytics consumers learn about a late table before they open a broken dashboard.
When to use it
Run this when downstream teams depend on a handful of canonical models (revenue, active users, pipeline) and you need a hard SLA promise like "refreshed within 90 minutes" rather than waiting for someone to notice numbers look old.
How it works
- 1A schedule fires every 15 minutes.
- 2The flow queries BigQuery for each watched model's max load/build timestamp (from `dbt_artifacts` or the table's `_dbt_updated_at` column).
- 3A logic step compares each model's age to its configured SLA threshold and keeps only the breaches.
- 4If any model is over SLA, it formats a breach summary with model name, age, and SLA.
- 5It posts the alert to the data-ops Slack channel, tagging the on-call owner.
Set it up
What you configure once, before turning it on.
- 1Connect BigQueryDatasets, queries, schemas.
- 2Connect SlackChannels, DMs, threads, mentions.
- 3Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 4Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 5Test, 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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