DATA OPS
Detect dbt Model Runtime Regressions and Alert Owners
After each dbt run it compares every model's build time against its recent baseline and flags models that suddenly got much slower, alerting the owning team in MS Teams before…
How it runs
The automated pipeline, trigger to output.
- Triggerdbt run completion posts timings to webhookHTTP webhook
- ActionStore timings and pull rolling baseline from SnowflakeSnowflake
- LogicFlag models exceeding runtime baseline multiple
- ActionAlert owning team in MS TeamsMicrosoft Teams
- OutputAppend regression trend to tracking tableSnowflake
What it does
Catches performance regressions, not just hard failures. It logs each model's run duration, compares it to a rolling baseline, and surfaces models whose runtime spiked, so a degrading query gets attention before it causes a missed pipeline SLA.
When to use it
Use it when your dbt DAG is large and a single slow model can push the whole run past its window. Performance regressions rarely error out, so they go unnoticed until the pipeline is late.
How it works
- 1A dbt run completion webhook posts per-model timings.
- 2The flow writes the timings to Snowflake and pulls each model's rolling median runtime.
- 3A logic step flags models exceeding their baseline by a configurable multiple.
- 4For flagged models it looks up the owning team and posts a regression alert to that team's MS Teams channel with current vs. baseline timing.
- 5A trend summary is appended to a tracking table for later review.
Set it up
What you configure once, before turning it on.
- 1Connect HTTP webhookTrigger any URL on agent actions.
- 2Connect SnowflakeWarehouses, queries, shares.
- 3Connect Microsoft TeamsChannels, chats, files.
- 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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