DATA OPS
Daily warehouse top-spender digest to Slack
Every morning, ranks yesterday's most expensive BigQuery query authors by bytes billed and posts a leaderboard to Slack so the data team sees who is driving spend before the bill…
How it runs
The automated pipeline, trigger to output.
- TriggerDaily 8am schedule
- ActionAggregate yesterday's job cost per author from INFORMATION_SCHEMA.JOBSBigQuery
- LogicRank authors by cost, keep top 10 with priciest query
- OutputPost leaderboard digest to Slack channelSlack
What it does
Queries BigQuery's `INFORMATION_SCHEMA.JOBS` for the prior day, aggregates total bytes billed and estimated dollar cost per user, and posts a ranked top-10 leaderboard to a Slack channel. Each row shows the author, job count, total cost, and the single most expensive query.
When to use it
Run this when warehouse spend is creeping up and nobody knows who owns it. A daily public leaderboard creates gentle social pressure and gives the data platform team an at-a-glance view of cost concentration without opening the billing console.
How it works
- 1A scheduled trigger fires each morning at 8am.
- 2A BigQuery action runs an aggregation over `INFORMATION_SCHEMA.JOBS` for the previous calendar day, grouping by `user_email` and computing `SUM(total_bytes_billed)` converted to dollars at the on-demand rate.
- 3A logic step sorts authors by cost descending and keeps the top 10, attaching each author's single priciest query text.
- 4A Slack action posts the formatted leaderboard with totals and per-author breakdown to the data-ops channel.
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.
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.