DATA OPS

Open a GitHub Issue When a BigQuery Source Table Breaks dbt Models

Watches BigQuery source table schemas daily and, when a breaking change like a dropped or retyped column is detected, opens a GitHub issue on the analytics repo tagging…

CategoryData Ops
Enginesim
Difficultyintermediate
Triggerschedule
Steps5
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerDaily schedule starts schema check
  • ActionFetch BigQuery source table metadataGoogle BigQueryBigQuery
  • LogicKeep only breaking changes (dropped or retyped columns)
  • ActionResolve dependent dbt models and owners
  • OutputOpen assigned GitHub issue on the analytics repoGitHubGitHub

What it does

Monitors BigQuery source tables for breaking schema changes and files a tracked GitHub issue against your analytics repository whenever one appears. The issue names the table, the exact change, and the dbt models that reference it, with owners assigned so the work lands in someone's queue.

When to use it

Use it when your team manages drift through pull requests and issues rather than chat pings, and you want a durable, assignable record instead of a Slack message that scrolls away. Best for breaking changes that demand a code fix.

How it works

  1. 1A daily schedule kicks off the schema check.
  2. 2BigQuery's table metadata is fetched for each registered source table.
  3. 3A logic step classifies the diff and keeps only breaking changes — dropped columns or incompatible type changes — discarding additive ones.
  4. 4For each breaking change, the dependent dbt models and their owners are looked up.
  5. 5A GitHub issue is opened on the analytics repo with the diff, impacted models, and owners assigned as assignees and labels.

Set it up

What you configure once, before turning it on.

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

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