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Elementary posts a structured data quality comment on every pull request that touches your dbt models, giving reviewers live context on test history, incidents, downstream impact, and merge risk — without leaving the PR.
Elementary data quality review comment on a pull request

Elementary data quality review comment on a pull request

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What the review includes

  • Change analysis: flags NULL risks, type mismatches, and join changes that could alter row counts
  • Performance & cost: SQL anti-pattern detection (SELECT *, cross joins, missing filters on large tables) with volume context
  • Downstream blast radius: which models, pipelines, and dashboards depend on what’s changing, including column-level impact for renamed or removed columns
  • Tests & incidents: pass/fail history for each changed model, active data quality incidents, and coverage gaps on new columns
  • Risk summary: a plain-language assessment of whether it’s safe to merge, with prioritized recommendations
The comment is concise when everything looks clean. It expands only when there are actual issues to flag, and updates automatically on every new push.

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How it works

When a PR is opened or updated:
  1. A CI job sends the repository name and branch to Elementary’s API
  2. Elementary fetches the diff, runs static SQL analysis, and queries live data quality context for the changed models
  3. A structured Markdown comment is posted on the PR or MR

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Setup

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Prerequisites

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GitHub Actions

1

Add the workflow file

Create .github/workflows/elementary-review.yml in your dbt repository:
2

Add a repository secret

Go to Settings > Secrets and variables > Actions in your GitHub repository and add:The review only runs on PRs that touch model files. Other PRs are ignored.
This works for pull requests opened from branches within the same repository. GitHub does not pass repository secrets to pull_request workflows triggered by forks or Dependabot.
If your repository is connected to multiple Elementary environments, add env-id to specify which one to use:
The environment ID is the UUID in your Elementary Cloud URL. For example, in https://app.elementary-data.com/169e9308-9a70-4200-b810-ad486cb42f3a/report/dashboard, the environment ID is 169e9308-9a70-4200-b810-ad486cb42f3a.If you don’t specify an env-id and the repository is connected to only one environment, it will be selected automatically. If connected to multiple, the review will return an error listing the available environment IDs.

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GitLab CI

1

Add the include to your .gitlab-ci.yml

2

Add CI/CD variables

Go to Settings > CI/CD > Variables and add:To post the MR comment, the template uses one of two authentication methods:
  • CI_JOB_TOKEN (default): GitLab’s built-in job token, available automatically in every pipeline. Requires a project admin to enable Settings > CI/CD > Token Access > Allow CI/CD job tokens to access this project’s API.
  • GITLAB_API_TOKEN (alternative): If this variable is set, it takes priority over CI_JOB_TOKEN. Use a Project Access Token with api scope. This works without any admin settings change and is the easier option if you don’t have project admin access.
Elementary data quality review comment on a GitLab merge request

Elementary data quality review comment on a GitLab merge request

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Troubleshooting

No comment appears after the job runs Make sure contents: read, pull-requests: write, and issues: write are set under permissions in the workflow. An explicit permissions block sets any unlisted scope to none, so omitting a required scope causes the step to fail silently. The review says the repository is not connected Go to Settings > Environments in Elementary Cloud and verify your repository is connected. The review says the repository is connected to multiple environments Add elementary-env-id to your workflow. The error message lists the available environment IDs.
If a model has never been synced through Elementary, the comment will note that no history is available yet. Results populate automatically after the next Elementary sync.