In practice¶
These standards aren't aspirational. Qualixto applies them on every engagement, and they're built into open-source starting points you can use today.
Templates that implement the standards¶
| Repository | What it gives you | Standards it implements |
|---|---|---|
| python-template | A Copier template: uv, ruff, strict mypy, pytest with a coverage gate, pre-commit with secrets scanning, dependency audits, a tested Docker image and CI | Python Standards, CI/CD Pipeline, Commit Message Standards, Definition of Done, Security Excellence |
| data-platform-starter | A runnable platform: dlt, DuckDB or MotherDuck, dbt and Dagster, with data contracts, severity-based quality gates and branch-isolated builds | Data Excellence, SQL Standards, Testing Pyramid, Architecture Decision Records |
Assessing a platform¶
The maturity model turns these pages into a scorecard. An assessment scores each pillar from 1 (Emerging) to 5 (Optimised), using evidence from the code, the pipelines and the team's own practices. It names the biggest gap and a quick win per pillar, and finishes with a prioritised plan.
Most teams don't need to reach level 5 everywhere. They need to know where they are, which gaps cost them most, and what to do first.
Working with Qualixto¶
Qualixto is an independent consultancy for hands-on data platform engineering. We build platforms with your team and leave them with the standards to run them.