DATA OPS
Emit dbt Source Freshness Lag as Datadog Metrics with SLA Monitors
On a schedule, measures how far behind each Snowflake source is versus its SLA and pushes the lag as a gauge metric to Datadog.
How it runs
The automated pipeline, trigger to output.
- TriggerEvery 15 minutes (schedule)
- ActionQuery latest load time per source from SnowflakeSnowflake
- LogicCompute lag-over-SLA minutes, tagged by source
- ActionSubmit gauge metrics to DatadogDatadog
- OutputReturn emitted metric set for audit
What it does
This workflow converts source freshness into a first-class observability signal. For each tracked Snowflake source it computes lag-versus-SLA in minutes and pushes it to Datadog as a tagged gauge metric. Once the metric flows, you build Datadog dashboards and monitors on top of it like any other SLI, instead of bolting alerting logic into the pipeline.
When to use it
Use it when your team already lives in Datadog for observability and wants data freshness to sit alongside service metrics, with alert thresholds, anomaly detection, and SLO tracking handled by Datadog itself.
How it works
- 1A schedule runs every 15 minutes.
- 2Query Snowflake for the latest load timestamp per tracked source.
- 3A logic step computes lag-over-SLA in minutes for each source, tagged by source and owner.
- 4Submit each value to Datadog as a gauge metric via the metrics API.
- 5Output the emitted metric set so the run is auditable.
Set it up
What you configure once, before turning it on.
- 1Connect SnowflakeWarehouses, queries, shares.
- 2Connect DatadogMetrics, traces, log search.
- 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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