DATA OPS
dbt source freshness watcher with severity-routed alerts
Checks Snowflake loaded-at timestamps against each dbt source's freshness SLA, then routes warnings to Slack and hard breaches to a PagerDuty incident so stale data never…
How it runs
The automated pipeline, trigger to output.
- TriggerSchedule every 30 minutes
- ActionLoad source freshness config from GitLabGitLab
- ActionQuery Snowflake for latest loaded-at per sourceSnowflake
- LogicBucket sources as fresh / warn / error by SLA
- ActionOpen PagerDuty incident for error-level breachesPagerDuty
- OutputPost freshness digest to SlackSlack
What it does
Reads the freshness thresholds (`warn_after` / `error_after`) declared on each dbt source, queries Snowflake for the max load timestamp of each source table, and compares the lag against the SLA. Fresh sources are ignored; warn-level lag pings Slack; error-level lag opens a PagerDuty incident so on-call can react before downstream models run on stale inputs.
When to use it
Run it on a tight cadence (every 30-60 min) for pipelines where data staleness causes real harm — finance close, ops dashboards, ML feature tables. Use it when `dbt source freshness` alone isn't loud enough to wake anyone up.
How it works
- 1A frequent schedule triggers the check.
- 2Pull source freshness config (table, loaded-at column, thresholds) from the dbt project in GitLab.
- 3Run a Snowflake query for the latest loaded-at per source table.
- 4Compute lag and bucket each source as fresh, warn, or error.
- 5Branch: warn sources go to a Slack message; error sources trigger a PagerDuty incident with the table and lag.
- 6Emit a combined freshness digest to Slack.
Set it up
What you configure once, before turning it on.
- 1Connect GitLabRepos, MRs, pipelines, registry.
- 2Connect SnowflakeWarehouses, queries, shares.
- 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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