DATA OPS
On-ingest Postgres PII guard via load webhook
Fires the moment a new table loads into Postgres, samples every column, and immediately revokes app-role access to any table with unmasked PII before downstream services can read…
How it runs
The automated pipeline, trigger to output.
- TriggerLoad-complete webhook with table nameHTTP webhook
- ActionSample columns of the new Postgres tablePostgres
- LogicClassify; proceed only on PII match
- ActionRevoke app-role access to quarantinePostgres
- OutputAnnounce catch in SlackSlack
What it does
Instead of scanning on a timer, this template hooks your ingestion pipeline: when a load job finishes it calls a webhook with the new table name. The workflow samples each column, classifies for sensitive data, and if it finds unmasked PII it revokes the application role's access on that table right away, so no API or dashboard can read the data before review. The detection is announced in Slack.
When to use it
Use it when freshly loaded tables become readable by production services within minutes and a once-a-day scan is too slow. Best when your loader can hit a webhook on completion.
How it works
- 1The load pipeline posts the new table name to the webhook trigger.
- 2Query Postgres for that table's columns and sample values from each.
- 3Classify samples and branch only when PII is present.
- 4Revoke the application role's privileges on the table to quarantine it.
- 5Post the table, matched columns, and categories to Slack for the on-call data owner.
Set it up
What you configure once, before turning it on.
- 1Connect HTTP webhookTrigger any URL on agent actions.
- 2Connect PostgresAny Postgres URL — query, write, migrate.
- 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
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.