DATA OPS
Snowflake schema drift sentinel with ClickUp remediation tickets
Snapshots Snowflake table definitions on a schedule, diffs them against the last known-good baseline.
How it runs
The automated pipeline, trigger to output.
- TriggerHourly schedule fires
- ActionQuery Snowflake INFORMATION_SCHEMA.COLUMNSSnowflake
- ActionLoad previous baseline snapshotPostgres
- LogicDiff and classify additive vs breaking changes
- LogicBranch only if breaking change found
- ActionOpen ClickUp remediation ticket with diffClickUp
- OutputPersist new snapshot as baselinePostgres
What it does
Watches a set of Snowflake tables and detects structural drift between runs. It distinguishes additive changes (new nullable columns) from breaking changes (dropped columns, narrowed types, new NOT NULL constraints) and only escalates the breaking ones into a tracked ClickUp ticket so on-call data engineers aren't paged for harmless additions.
When to use it
Use it when downstream models, dashboards, or reverse-ETL jobs read from warehouse tables you don't fully control — for example tables loaded by Fivetran or a partner's pipeline. It catches the silent break before a dbt run fails or a dashboard goes blank.
How it works
- 1A scheduled trigger fires (e.g. hourly).
- 2Query Snowflake `INFORMATION_SCHEMA.COLUMNS` for the watched schema and capture the current column set, types, and nullability.
- 3Compare the snapshot against the stored baseline from the previous run.
- 4Classify each delta: additive vs breaking.
- 5If any breaking deltas exist, branch to remediation; otherwise update the baseline and exit quietly.
- 6Open a ClickUp task with the diff, affected tables, and a suggested owner.
- 7Persist the new snapshot as the next baseline.
Set it up
What you configure once, before turning it on.
- 1Connect SnowflakeWarehouses, queries, shares.
- 2Connect ClickUpDocs + tasks + chats in one workspace.
- 3Connect PostgresAny Postgres URL — query, write, migrate.
- 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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