DATA OPS
Multi-Warehouse PII Drift Register in Airtable
Sweeps both BigQuery and Snowflake on a schedule, consolidates every newly detected sensitive column into a single Airtable data-governance register.
How it runs
The automated pipeline, trigger to output.
- TriggerScheduled run starts the multi-warehouse sweep
- ActionQuery columns and samples from BigQuery and SnowflakeBigQuery
- LogicDiff each source against baseline and classify PIIPostgres
- LogicMerge and dedupe findings across warehouses
- ActionUpsert new sensitive columns into Airtable registerAirtable
- OutputPost consolidated drift digest to SlackSlack
What it does
This workflow maintains one canonical PII register across multiple warehouses. It scans both BigQuery and Snowflake, deduplicates and records each newly appeared sensitive column as a row in an Airtable governance register, and sends stewards a single digest rather than a flood of per-column alerts.
When to use it
Use it when sensitive data lives in more than one warehouse and you need a unified, auditable inventory of every PII field plus its review status, instead of scattered tickets across teams.
How it works
- 1A scheduled run kicks off the multi-warehouse sweep.
- 2The workflow queries column metadata and samples from BigQuery and Snowflake in parallel.
- 3It diffs each source against its stored baseline in Postgres and classifies new columns for PII.
- 4Findings are merged and deduplicated across warehouses into a normalized set.
- 5Each new sensitive column is upserted as a row in the Airtable register with source, category, and an open review status.
- 6A consolidated drift digest is posted to Slack so stewards see the full week's changes at a glance.
Set it up
What you configure once, before turning it on.
- 1Connect BigQueryDatasets, queries, schemas.
- 2Connect SnowflakeWarehouses, queries, shares.
- 3Connect PostgresAny Postgres URL — query, write, migrate.
- 4Connect AirtableBases, tables, views, automations.
- 5Connect SlackChannels, DMs, threads, mentions.
- 6Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 7Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 8Test, 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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