DATA OPS

Dropped-column impact analysis with Linear issue and owner notification

When a Snowflake column is dropped or renamed, traces which downstream tables and dbt models reference it, files a Linear issue scoped to the impact.

CategoryData Ops
EngineSim + Paperclip
Difficultyadvanced
Triggerschedule
Steps6
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerScheduled drift scan fires
  • ActionDetect dropped or renamed Snowflake columnsSnowflakeSnowflake
  • LogicExit if no destructive change
  • ActionSearch dbt repo for column referencesGitHubGitHub
  • ActionFile scoped Linear issue with impact listLinearLinear
  • OutputDM affected model owners on SlackSlack

What it does

Goes beyond detecting a dropped column — it figures out the blast radius. On detecting a removed or renamed column, it scans your dbt project in GitHub for references to that column, builds an impact list of affected models and their owners, files a Linear issue with that impact attached, and notifies each owner directly.

When to use it

Use it when a single source-table change can cascade through dozens of dependent models and you need the remediation routed to the right people with the full dependency picture, not just a generic 'schema changed' alert.

How it works

  1. 1A scheduled trigger fires.
  2. 2Detect dropped or renamed columns in Snowflake versus the last baseline.
  3. 3If none, exit; if found, search the dbt repo in GitHub for references to each affected column.
  4. 4Build the impact list of downstream models and their declared owners.
  5. 5Create a Linear issue with the column change and the impacted-model list.
  6. 6DM each owner on Slack with their specific affected models and the Linear link.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect SnowflakeWarehouses, queries, shares.
  2. 2
    Connect GitHubRepos, issues, pull requests, actions.
  3. 3
    Connect LinearIssues, projects, cycles, triage.
  4. 4
    Connect SlackChannels, DMs, threads, mentions.
  5. 5
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  6. 6
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  7. 7
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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