DATA OPS
BigQuery Slot-Hour Spike Attributor
Runs every morning, compares yesterday's BigQuery slot-hour consumption against the trailing 14-day baseline.
How it runs
The automated pipeline, trigger to output.
- TriggerDaily 07:00 schedule
- ActionQuery yesterday's slot-ms by scheduled queryBigQuery
- ActionQuery trailing 14-day baseline per queryBigQuery
- LogicFlag queries over +40% or +5 slot-hours
- OutputPost offending query + job ID to SlackSlack
What it does
Detects day-over-day slot-hour regressions in BigQuery scheduled queries and attributes the spike to the single query that changed, so on-call data engineers see *which* query blew the budget, not just *that* it did.
When to use it
When your warehouse bill creeps up and nobody knows which scheduled query is responsible. Run it daily so a regression is caught the morning after it ships, not at the end of the billing cycle.
How it works
- 1A scheduled trigger fires each morning at 07:00.
- 2A BigQuery query reads `INFORMATION_SCHEMA.JOBS` for yesterday's slot-ms grouped by scheduled-query label.
- 3A second BigQuery query pulls the trailing 14-day average per label as a baseline.
- 4A logic step computes per-query delta and flags any query exceeding +40% slot-hours or +5 absolute slot-hours.
- 5If nothing crosses the threshold, the run exits quietly.
- 6A Slack message lists the top offending query, its job ID, yesterday vs. baseline slot-hours, and the percent jump.
Set it up
What you configure once, before turning it on.
- 1Connect BigQueryDatasets, queries, schemas.
- 2Connect SlackChannels, DMs, threads, mentions.
- 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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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.

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