v0.2.2: Added alerting and documentation

Added

• CLI Reference: Added detailed documentation for init, run, build, and lint commands.
• Python API Guide: Added guide for using run_project in Python scripts (e.g., for Databricks Jobs).
• Alerting: Added support for sending notifications to a webhook URL on run completion or failure.

Changed

• README: Updated README.md with a direct link to the full documentation and updated example SQL models.

Fixed

• UI: Fixed spacing issues in the "Features" section of the documentation homepage.

v0.2.1 - Fail Soft Execution & Global Logging

🚀 New Features

• Fail-Soft Execution: Model failures no longer abort runs
• Independent Execution: Unrelated models continue
• Smart Skips: Downstream dependencies marked SKIP
• Final Summary: PASS / WARN / ERROR / SKIP counts
• Structured Logging:
  • Replaced print with Python logging
  • HH:MM:SS Message format
  • SQL RUNNER header and progress indicators

🛠 Improvements & Fixes

• CLI: Cleaner output; fixed unused variables in cli.py
• Docs: Improved README.md; added PR template
• Tests: Updated fail-soft coverage

📦 Packaging

• Version bumped to 0.2.1
• Build artifacts verified

v0.2.0: Added scaffolding & linting

🚀 Features

• Init Command: Added dbx-sql-runner init to scaffold new projects with best-practice structure, configurable profiles.yml, and default linter config.
• Linter: Added dbx-sql-runner lint to enforce snake_case naming conventions for models, sources, and columns. Includes SQL parsing for column validation.

🧪 Tests & CI

• Automated Testing: Added pytest suite covering scaffold generation and linter logic.
• CI/CD: Added GitHub Actions workflow to run tests and ruff linting on every Pull Request.

🔧 Chores

• Cleanup: Updated .gitignore to exclude dev artifacts.
• Docs: Updated README.md with development and testing instructions.

v0.1.0: Initial Base Release

This is the initial base version of dbx-sql-runner.

🌟 Key Features

• Simple SQL Models: Just write .sql files. No complex boilerplate.
• Automated Dependency Management: Reference other models using {upstream_model} and let the runner build the DAG for you.
• Environment Aware: Seamlessly switch between Dev and Prod using profiles.yml and Environment Variables.
• Library Design: Import dbx_sql_runner in your Python scripts (great for Airflow/Databricks Jobs) or run it via CLI.

📦 Installation

pip install dbx-sql-runner==0.1.0