DATA OPS

Snowflake Schema-Drift Sentinel to ClickUp Fix Task

Runs nightly, diffs live Snowflake column types and nullability against your dbt model contract.

CategoryData Ops
Enginesim
Difficultyintermediate
Triggerschedule
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerNightly schedule before dbt build
  • ActionQuery Snowflake INFORMATION_SCHEMA for live column shapesSnowflakeSnowflake
  • ActionLoad dbt contract definitions from repoGitHubGitHub
  • LogicDiff live columns vs. contract; flag adds/drops/retypes
  • LogicExit quietly if no drift detected
  • OutputOpen ClickUp fix task with column deltas per modelClickUpClickUp

What it does

This workflow catches silent schema drift in Snowflake before it breaks dbt builds or dashboards. Each night it pulls the actual column set for your contracted models from `INFORMATION_SCHEMA`, compares it to the column names, data types, and nullability declared in your dbt contract, and files a single ClickUp task enumerating every mismatch. Clean runs produce nothing, so the only signal an operator ever sees is real drift.

When to use it

Use it when upstream teams own the tables your dbt models read and can change columns without telling you. It is the right fit for warehouses where a renamed or retyped column would otherwise surface as a failed dbt run hours later, or as a wrong number on a board.

How it works

  1. 1A nightly schedule fires before the dbt build window.
  2. 2Query Snowflake `INFORMATION_SCHEMA.COLUMNS` for every contracted model's live shape.
  3. 3Load the committed dbt contract definitions from the repo.
  4. 4Diff live vs. contract: flag added, dropped, retyped, and nullability-changed columns.
  5. 5If the diff is empty, exit quietly.
  6. 6Open a ClickUp task per affected model with the exact column deltas and the owning team assigned.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect SnowflakeWarehouses, queries, shares.
  2. 2
    Connect ClickUpDocs + tasks + chats in one workspace.
  3. 3
    Connect GitHubRepos, issues, pull requests, actions.
  4. 4
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  5. 5
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  6. 6
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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