SQLFluff in the Wild

Want to find other people who are using SQLFluff in production use cases? Want to brag about how you’re using it? Just want to show solidarity with the project and provide a testimonial for it?

Just add a section below by raising a PR on GitHub by editing this file ✏️.

  • SQLFluff in production dbt projects at tails.com. We use the SQLFluff cli as part of our CI pipeline in codeship to enforce certain styles in our SQL codebase (with over 650 models) and keep code quality high. Contact @alanmcruickshank.

  • Netlify’s data team uses SQLFluff with dbt to keep code quality in more than 350 models (and growing). Previously, we had our SQL Guidelines defined in a site hosted with Netlify, and now we’re enforcing these rules in our CI workflow thanks to SQLFluff.

  • Drizly’s analytics team uses SQLFluff with dbt for over 700 models as part of our CI checks in GitHub. Before SQLFluff, we had SQL best practices outlined in a google doc and had to manually enforce through PR comments. We’re now able to enforce much of our style guide automatically through SQLFluff.

  • Petal’s data-eng team runs SQLFluff on our 100+ model dbt project. As a pre-commit hook and as a CI check, SQLFluff helps keep our SQL readable and consistent.

  • Surfline’s Analytics Engineering team implemented SQLFluff as part of our continuous integration (CI) suite across our entire dbt project (700+ models). We implement the CI suite using GitHub Actions and Workflows. The benefits of using SQLFluff at Surfline are:

    • The SQL in our dbt models is consistent and easily readable.

    • Our style guide is maintained as code, not a README that is rarely updated.

    • Reduced burden on Analytics Engineers to remember every single style rule.

    • New Analytics Engineers can quickly see and learn what “good SQL” looks like at Surfline and start writing it from day 1.

  • The HTTP Archive uses SQLFluff to automatically check for quality and consistency of code submitted by the many contributors to this project. In particular our annual Web Almanac attracts hundreds of volunteers to help analyse our BigQuery dataset and being able automatically lint Pull Requests through GitHub Actions is a fantastic way to help us maintain our growing repositary of over a thousand queries.