by andre-salvati · Claude Skill · ★ 72
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databricks-template A production-ready PySpark project template with medallion architecture, Python packaging, unit tests, integration tests, coverage tests, CI/CD automation, Declarative Automation Bundles, and DQX data quality framework. 🚀 Overview This project template is designed to boost productivity and promote maintainability when developing ETL pipelines on Databricks. It aims to bring software engineering best practices—such as modular architecture, automated unit and integration testing, and CI/CD—into the world of data engineering. By combining a clean project structure with robust development and deployment jobs, this template helps teams move faster with confidence. You’re encouraged to adapt the structure and tooling to suit your project’s specific needs and environment. Interested in bringing these principles in your own project? Let’s connect on Linkedin. 🧪 Technologies Databricks Free Edition (Serverless) Databricks Runtime 18.0 LTS Databricks Unity Catalog Databricks Declarative Automation Bundles (for
| Stars | 72 |
| Forks | 31 |
| Language | Python |
| Category | Claude Skill |
| Quality Score | 39.61/100 |
| Last Updated | 2026-06-12 |
| Created | 2023-12-23 |
| Platforms | claude-code, cli, python |
| Est. Tokens | ~16k |
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databricks-template is A production-ready PySpark project template with medallion architecture, Python packaging, unit tests, integration tests, CI/CD automation, Databricks Asset Bundles, and DQX data quality framework.. It is categorized as a Claude Skill with 72 GitHub stars.
databricks-template is primarily written in Python. It covers topics such as apache-spark, claude-code, dashboard.
You can find installation instructions and usage details in the databricks-template GitHub repository at github.com/andre-salvati/databricks-template. The project has 72 stars and 31 forks, indicating an active community.
The top alternatives to databricks-template on Agent Skills Hub include ring, KARIMO, skills. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.