DATA OPS
Live BigQuery DDL Drift Alert to Slack
Listens for BigQuery table-change events via webhook, diffs the new schema against the last known contract, and posts a formatted drift alert to a Slack channel in near real time.
How it runs
The automated pipeline, trigger to output.
- TriggerWebhook receives BigQuery table-change eventHTTP webhook
- ActionFetch current BigQuery table schemaBigQuery
- LogicDiff against cached contract; drop no-op events
- OutputPost column-level drift alert to SlackSlack
What it does
Turns BigQuery schema changes into immediate Slack alerts. Instead of waiting for a nightly scan, it reacts to a DDL change event the moment a tracked table is altered, computes what changed, and drops a clear message into your data-platform channel with the column-level diff.
When to use it
Use it when latency matters — when a surprise `ALTER TABLE` can break a live pipeline within minutes and your team needs to know now, not tomorrow morning. Best paired with a BigQuery log sink or audit export wired to the webhook.
How it works
- 1A webhook receives a BigQuery table-change event (from a log sink or audit notification).
- 2The workflow fetches the current schema for the named table from BigQuery.
- 3It diffs the fetched schema against the cached contract for that table.
- 4A logic step suppresses no-op or metadata-only events so Slack stays quiet on non-changes.
- 5It posts a Slack message with the table name, who changed it, and an added/removed/retyped column breakdown.
Set it up
What you configure once, before turning it on.
- 1Connect BigQueryDatasets, queries, schemas.
- 2Connect SlackChannels, DMs, threads, mentions.
- 3Connect HTTP webhookTrigger any URL on agent actions.
- 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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