DATA OPS
Attribute BigQuery slot contention to noisy query authors and nudge them in Slack
Each morning, ranks the previous day's BigQuery query authors by slot-milliseconds consumed, flags the top contention drivers during peak hours.
How it runs
The automated pipeline, trigger to output.
- TriggerDaily schedule after prior day closes
- ActionQuery JOBS for slot_ms by author (24h)BigQuery
- LogicRank authors, keep those over contention threshold
- ActionFetch each flagged author's costliest queriesBigQuery
- OutputDM each author their slot share and queriesSlack
- OutputPost ranked leaderboard to #data-platformSlack
What it does
It turns yesterday's BigQuery usage into a clear, person-by-person accountability report. The workflow pulls slot consumption from INFORMATION_SCHEMA, attributes it to the user who ran each job, and quietly nudges the heaviest consumers with the exact queries that drove their cost — before they repeat the pattern today.
When to use it
Use it when a shared reservation keeps hitting its slot ceiling and analysts blame "the warehouse being slow" instead of their own scans. It replaces finger-pointing with named, data-backed feedback delivered privately, so people self-correct without a manager meeting.
How it works
- 1A daily schedule fires after the prior day closes.
- 2Query `INFORMATION_SCHEMA.JOBS` for total_slot_ms, bytes scanned, and user_email grouped by author for the last 24h.
- 3Rank authors and keep only those above the contention threshold during peak hours.
- 4For each flagged author, fetch their three costliest individual queries.
- 5Send each author a private Slack DM with their slot share, dollar estimate, and the specific queries to optimize.
- 6Post a ranked leaderboard to the #data-platform channel for team visibility.
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.