DATA OPS
Correlate a BigQuery metric anomaly with Honeycomb traces before paging
When a BigQuery latency or error metric spikes, it queries Honeycomb for the matching time window to find the slowest traces and offending services.
How it runs
The automated pipeline, trigger to output.
- Trigger15-minute schedule fires
- ActionDetect metric spike over baseline in BigQueryBigQuery
- ActionQuery Honeycomb for slow traces in the same windowHoneycomb
- LogicBranch on whether traces confirm a regression
- ActionPage on-call via PagerDuty with correlated evidencePagerDuty
- OutputPost low-priority Slack note when unconfirmedSlack
What it does
It watches a service-health metric in BigQuery and, on a spike, immediately cross-references Honeycomb traces from the same minute window. It surfaces the slowest operations and the services contributing most to the anomaly, then decides whether to page. If the traces show a genuine backend regression it fires PagerDuty with the correlated evidence attached; if traces look clean it downgrades to a Slack heads-up instead.
When to use it
Use it when your warehouse metrics and your tracing data tell two halves of the same story and you want them stitched together automatically. It cuts the noisy 3am page when the metric blip was just a reporting lag rather than a real outage.
How it works
- 1A schedule polls the metric every 15 minutes.
- 2BigQuery returns the metric value and detects a spike over baseline.
- 3Honeycomb is queried for the same window to rank slow traces and services.
- 4A logic step decides: traces confirm a regression or not.
- 5Confirmed cases page PagerDuty with the correlated evidence.
- 6Unconfirmed cases post a low-priority Slack note for visibility.
Set it up
What you configure once, before turning it on.
- 1Connect BigQueryDatasets, queries, schemas.
- 2Connect HoneycombDistributed traces and queries.
- 3Connect PagerDutyIncidents, on-call, escalations.
- 4Connect SlackChannels, DMs, threads, mentions.
- 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.
More Data Ops workflows
Weekly BigQuery Cost Trend Sheet and Exec Digest
Compiles week-over-week BigQuery scheduled-query cost by owner and dataset into a Google Sheet with trend columns.
Daily BigQuery Scheduled-Query Cost Attribution to Owners
Each morning, totals the prior day's on-demand bytes-billed per scheduled query, maps each query to its owner from a label, and posts a per-owner cost leaderboard to Slack.
BigQuery Per-Team Budget Breach Alert to PagerDuty
Tracks month-to-date BigQuery scheduled-query spend per team and, when a team crosses its monthly budget, pages the team's on-call in PagerDuty and snapshots the spend breakdown…
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…
dbt orphan model detector with Linear cleanup tickets
Scans your dbt manifest for models that no other model, exposure, or BI tool consumes.
Raw Sensor Telemetry Archive to BigQuery
Captures every incoming building sensor reading via webhook, normalizes the payload into a consistent schema.
Run it inside a business
This workflow drops into a full company template. Import the org, and this is one of the playbooks its agents run.
Run this workflow in your colony.
14-day trial. No DevOps. No Sales call. Provisioned in under a minute.