https://github.com/PicnicSupermarket/dbt-score

Linter for dbt metadata
https://github.com/PicnicSupermarket/dbt-score

dbt dbt-core linter metadata python

Last synced: 10 days ago
JSON representation

Linter for dbt metadata

• Host: GitHub
• URL: https://github.com/PicnicSupermarket/dbt-score
• Owner: PicnicSupermarket
• License: mit
• Created: 2024-02-13T13:42:50.000Z (5 months ago)
• Default Branch: master
• Last Pushed: 2024-06-17T08:27:54.000Z (11 days ago)
• Last Synced: 2024-06-17T08:27:55.664Z (11 days ago)
• Topics: dbt, dbt-core, linter, metadata, python
• Language: Python
• Homepage: https://dbt-score.picnic.tech
• Size: 892 KB
• Stars: 48
• Watchers: 32
• Forks: 2
• Open Issues: 7
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.md
  • Contributing: docs/CONTRIBUTING.md
  • License: LICENSE.txt

Lists

• awesome-dbt - dbt-score - Linter for dbt metadata. (Utilities)
• awesome-dbt - dbt-score - Linter for dbt metadata. (Utilities)

README

        
# dbt-score

[![CI](https://github.com/PicnicSupermarket/dbt-score/actions/workflows/ci.yml/badge.svg)](https://github.com/PicnicSupermarket/dbt-score/actions)

[![PyPI version](https://img.shields.io/pypi/v/dbt-score.svg)](https://pypi.python.org/pypi/dbt-score/)

[![PyPI license](https://img.shields.io/pypi/l/dbt-score.svg)](https://pypi.python.org/pypi/dbt-score/)

[![Docs](https://img.shields.io/badge/Docs-mkdocs-blue)](https://dbt-score.picnic.tech/)

[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/dbt-score.svg)](https://pypi.org/project/dbt-score)

[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg)](https://makeapullrequest.com)

![dbt-score-output](images/dbt-score-output.png)

## What is `dbt-score`?

`dbt-score` is a linter for dbt model metadata.

[dbt][dbt] (Data Build Tool) is a great framework for creating, building,

organizing, testing and documenting _data models_, i.e. data sets living in a

database or a data warehouse. Through a declarative approach, it allows data

practitioners to build data with a methodology inspired by software development

practices.

This leads to data models being bundled with a lot of metadata, such as

documentation, data tests, access control information, column types and

constraints, 3rd party integrations... Not to mention any other metadata that

organizations need, fully supported through the `meta` parameter.

At scale, with hundreds or thousands of data models, all this metadata can

become confusing, disparate, and inconsistent. It's hard to enforce good

practices and maintain them in continuous integration systems. This is

where`dbt-score` plays its role: by allowing data teams to programmatically

define and enforce metadata rules, in an easy and scalable manner.

## Documentation

Everything you need (and more) can be found in [`dbt-score` documentation

website][dbt-score].

## Contributing

Would you like to contribute to `dbt-score`? That's great news! Please follow

[the guide on the documentation website][contributors-guide]. 🚀

[dbt]: https://github.com/dbt-labs/dbt-core

[dbt-score]: https://dbt-score.picnic.tech/

[contributors-guide]: https://dbt-score.picnic.tech/contributors_guide