DATA OPS

Auto-File GitHub Issue for Unexplained dbt Freshness Misses

On a dbt run-failure webhook, checks BigQuery freshness and Honeycomb for an upstream cause, and opens a labeled GitHub issue only when the miss is unexplained by upstream lag.

CategoryData Ops
Enginesim
Difficultyadvanced
Triggerwebhook
Steps5
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • Triggerdbt run-failure webhook receivedHTTP webhook
  • ActionConfirm which failed models are over SLAGoogle BigQueryBigQuery
  • ActionCheck Honeycomb upstream latency in windowHoneycomb
  • LogicDrop upstream-explained, keep unexplained misses
  • OutputOpen labeled, owner-assigned GitHub issueGitHubGitHub

What it does

Closes the loop between detection and tracking. When dbt Cloud (or your orchestrator) fires a run-failure webhook, the flow confirms which models actually went stale in BigQuery, checks Honeycomb to see if an upstream slowdown explains it, and — only for genuinely unexplained failures — files a pre-triaged GitHub issue so nothing falls through the cracks.

When to use it

Use this when you want every real, owner-actionable dbt freshness failure to become a tracked engineering ticket automatically, without manually opening issues or creating noise for transient upstream delays.

How it works

  1. 1A dbt run-failure webhook triggers the flow with the failed run's models.
  2. 2BigQuery confirms which of those models are now past their freshness SLA.
  3. 3Honeycomb is queried for upstream latency in the failure window.
  4. 4A logic step suppresses upstream-explained misses and keeps unexplained ones.
  5. 5For each unexplained breach it opens a labeled, owner-assigned GitHub issue with logs and links.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect HTTP webhookTrigger any URL on agent actions.
  2. 2
    Connect BigQueryDatasets, queries, schemas.
  3. 3
    Connect HoneycombDistributed traces and queries.
  4. 4
    Connect GitHubRepos, issues, pull requests, actions.
  5. 5
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  6. 6
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  7. 7
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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