DATA OPS
Daily BigQuery to Snowflake Schema-Drift Report
Each morning it compares column names, types, and nullability between matched BigQuery and Snowflake tables.
How it runs
The automated pipeline, trigger to output.
- TriggerDaily schedule before standup
- ActionRead BigQuery column schemaBigQuery
- ActionRead Snowflake column schemaSnowflake
- LogicDiff and classify column changes
- LogicBranch: drift found vs clean
- OutputPost drift report to SlackSlack
What it does
On a daily schedule, this workflow pulls the live schema of a configured set of BigQuery tables and their Snowflake counterparts, diffs them column by column, and produces a single change report covering added columns, dropped columns, renamed-or-retyped columns, and nullability changes. The report is delivered to Slack so nobody has to manually eyeball `INFORMATION_SCHEMA` across two warehouses.
When to use it
Use it when BigQuery is your source of truth and Snowflake is a downstream replica or analytics mirror, and unannounced upstream schema changes keep breaking dbt models or BI dashboards. It turns silent drift into a morning heads-up.
How it works
- 1A scheduled trigger fires once per day before standup.
- 2It queries BigQuery `INFORMATION_SCHEMA.COLUMNS` for the watched dataset.
- 3It queries Snowflake `INFORMATION_SCHEMA.COLUMNS` for the mirrored schema.
- 4A logic step diffs the two column maps and classifies each difference.
- 5A branch checks whether any drift was found.
- 6The formatted drift report is posted to a Slack channel; a clean run posts a short all-clear.
Set it up
What you configure once, before turning it on.
- 1Connect BigQueryDatasets, queries, schemas.
- 2Connect SnowflakeWarehouses, queries, shares.
- 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.
