DATA OPS
BigQuery row-count anomaly detection with PagerDuty escalation
After each nightly BigQuery load, this compares today's partition row count against the trailing 30-day baseline using a z-score.
How it runs
The automated pipeline, trigger to output.
- TriggerNightly cron after ingestion
- ActionQuery today + trailing 30-day row countsBigQuery
- LogicCompute z-score and classify anomalies
- LogicBranch: escalate only on breach
- OutputOpen PagerDuty incident for on-callPagerDuty
What it does
Detects volume anomalies in freshly loaded BigQuery partitions. It pulls today's row count for each monitored table plus the trailing 30-day daily counts, computes a z-score against that baseline, and decides whether the load is suspiciously small (dropped rows), suspiciously large (duplicate load), or normal. Real anomalies escalate to PagerDuty; everything else stays quiet.
When to use it
Use it when a load "succeeds" but quietly writes the wrong amount of data — a truncated upstream extract or a double-run that doubles a fact table. Pure freshness checks miss these because the timestamp looks fine.
How it works
- 1A nightly cron fires after BigQuery ingestion completes.
- 2A query returns today's partition count and the trailing 30-day counts per table.
- 3A logic step computes the z-score and classifies each table as normal, low, or high.
- 4If any table breaches the threshold, an enriched incident payload is built with the expected range and observed count.
- 5A PagerDuty incident is opened and routed to the data on-call rotation.
Set it up
What you configure once, before turning it on.
- 1Connect BigQueryDatasets, queries, schemas.
- 2Connect PagerDutyIncidents, on-call, escalations.
- 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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