DATA OPS
BigQuery Column-Drift Sentinel that Pauses Dependent dbt Pipelines
Snapshots the column list and types of your watched BigQuery tables on a schedule, and when a column is added, dropped, or retyped it disables the downstream dbt/scheduled…
How it runs
The automated pipeline, trigger to output.
- TriggerSchedule fires every 30 minutes
- ActionRead current column schema from BigQuery INFORMATION_SCHEMABigQuery
- ActionLoad last stored fingerprint from PostgresPostgres
- LogicDiff schemas; branch on drift detected
- ActionDisable affected scheduled queries in BigQueryBigQuery
- OutputPost column diff and paused jobs to SlackSlack
What it does
This sentinel keeps a fingerprint of every watched BigQuery table — column names, ordinal positions, and data types pulled from `INFORMATION_SCHEMA.COLUMNS`. On each run it compares the live schema against the stored fingerprint. If anything changed, it pauses the dependent pipelines before they can run against the broken contract and notifies the owners with an exact diff.
When to use it
Use it when upstream tables are owned by another team (product, finance) who can rename or drop columns without warning, and a silent drift would corrupt your transformed models or dashboards. It buys you a clean failure instead of bad data flowing downstream.
How it works
- 1A schedule fires (e.g. every 30 minutes).
- 2Query `INFORMATION_SCHEMA.COLUMNS` for each watched BigQuery dataset to capture the current schema fingerprint.
- 3Compare against the last stored fingerprint in Postgres; compute added/dropped/retyped columns.
- 4Branch: if no diff, store the fingerprint and exit; if drift is detected, continue.
- 5Disable the affected scheduled queries / dbt jobs in BigQuery so they stop running.
- 6Post a structured diff to the data team's Slack channel with the table, the change, and the paused jobs.
Set it up
What you configure once, before turning it on.
- 1Connect BigQueryDatasets, queries, schemas.
- 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.
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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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