DATA OPS
Alert on per-team BigQuery spend spikes after attribution
Reads the attributed BigQuery spend table, compares each team's daily bytes billed against its trailing baseline.
How it runs
The automated pipeline, trigger to output.
- TriggerMorning schedule after attribution finalized
- ActionRoll up yesterday's attributed spend by team in BigQueryBigQuery
- LogicCompare to trailing baseline; keep teams over threshold + floor
- ActionFetch top costly query jobs for each flagged teamBigQuery
- OutputPost per-team spike alert to Slack with contextSlack
What it does
Turns clean per-team attribution into an early-warning signal: it rolls yesterday's attributed BigQuery spend up by team, compares it to each team's trailing average, and flags meaningful spikes.
When to use it
Run it once attribution is reliable. It catches the exploded backfill, the missing partition filter, or the runaway dashboard before it shows up as a surprise on the monthly invoice.
How it works
- 1A morning schedule fires after the prior day's attribution is finalized.
- 2Query the attribution table to total bytes billed and dollars per team for yesterday.
- 3Pull each team's trailing baseline (for example a 14-day median) from the same table.
- 4Branch: keep only teams whose spend exceeds the baseline by the configured multiplier and a dollar floor.
- 5For each flagged team, fetch its top query jobs by spend for context.
- 6Post a per-team Slack alert showing yesterday vs baseline, the delta, and the costliest queries to investigate.
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 this workflow in your colony.
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