DATA OPS
Validate BigQuery Audience Before Ad-Platform Sync
Runs schema and quality checks on a computed audience segment in BigQuery before pushing it to ad destinations.
How it runs
The automated pipeline, trigger to output.
- TriggerSchedule fires after nightly audience model build
- ActionQuery candidate segment from BigQueryBigQuery
- LogicSplit rows into pass/fail by null, format, consent, dedupe rules
- ActionWrite failing rows to BigQuery quarantine table with reason codesBigQuery
- ActionPush passing rows to ad destinationsSocial publishing
- OutputPost synced-vs-quarantined summary to SlackSlack
What it does
Before a computed marketing audience leaves your warehouse, this workflow inspects every row for the conditions ad platforms silently reject — null emails, malformed identifiers, opted-out contacts, and duplicate keys. Clean rows sync onward; failing rows land in a quarantine table with a reason code, and the team gets a summary in Slack.
When to use it
Use it whenever you publish a BigQuery segment to ad audiences and want to stop bad data from poisoning match rates or burning spend on invalid identifiers. Ideal as the guard step between your dbt models and your activation layer.
How it works
- 1A schedule fires after the nightly audience model finishes building.
- 2Query the candidate segment from BigQuery.
- 3A validation step splits rows into pass and fail buckets using null, format, consent, and dedupe rules.
- 4Failing rows are written back to a BigQuery quarantine table with reason codes.
- 5Passing rows are pushed to the ad destinations.
- 6A Slack message reports counts synced versus quarantined and the top failure reasons.
Set it up
What you configure once, before turning it on.
- 1Connect BigQueryDatasets, queries, schemas.
- 2Connect Social publishingCross-post to X, LinkedIn, Instagram, TikTok, and 4 more in one call.
- 3Connect SlackChannels, DMs, threads, mentions.
- 4Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 5Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 6Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
More Data Ops workflows
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 orphan model detector with Linear cleanup tickets
Scans your dbt manifest for models that no other model, exposure, or BI tool consumes.
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.
Backfill Missing Owner Labels on BigQuery Scheduled Queries
Finds scheduled queries with no owner label, infers the likely owner from creator metadata and target-table lineage, proposes a label.
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.
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…
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.
Creator
Social Manager
Draft, approve, schedule, and measure social posts across 8 channels.
Creator
Social Media Brand House
Run a multi-channel brand presence end to end — plan, draft, approve, publish, measure.
Operations
Event Series Operator
Run a recurring event series — promote, sell tickets, fill seats, follow up.
Run this workflow in your colony.
14-day trial. No DevOps. No Sales call. Provisioned in under a minute.