Snowflake Data Quality Assessment Framework A comprehensive framework for assessing and improving data quality in Snowflake databases, utilizing key data quality dimensions to provide a data maturity score and detailed insights.

Table of Contents Introduction Features Demo Installation Usage Dependencies Configuration Screenshots Contributing License Contact Introduction Data quality is critical for any data-driven organization. This framework provides an easy-to-use application to assess the quality of data stored in Snowflake databases. By evaluating data across multiple dimensions, the tool generates a data maturity score and offers insights to help improve data quality.

Features Data Maturity Score: Calculates an overall score based on various data quality dimensions. Multiple Data Quality Dimensions: Assesses data using key dimensions such as Accuracy, Completeness, Consistency, Timeliness, Uniqueness, and Validity. Interactive Streamlit Application: User-friendly interface built with Streamlit for easy interaction. Detailed Reports: Generates comprehensive reports highlighting areas of improvement. Snowflake Integration: Seamless connection to Snowflake databases for real-time assessments. Extensible Framework: Easily add new data quality checks and dimensions as needed.

Installation Follow these steps to set up the application:

Clone the Repository

bash Copy code git clone https://github.com/YourUsername/snowflake-data-quality-framework.git cd snowflake-data-quality-framework

Paste the code in snowflake streamlit section and hit RUN!!!

Screenshots Home Page

Data Maturity Score

Detailed Reports

Contributing Contributions are welcome! Please follow these steps:

Fork the repository. Create a new branch: git checkout -b feature/your-feature-name. Commit your changes: git commit -m 'Add some feature'. Push to the branch: git push origin feature/your-feature-name. Open a pull request. Please read the CONTRIBUTING.md file for more details on our code of conduct and submission guidelines.

License This project is licensed under the MIT License - see the LICENSE file for details.

Contact Author: Mohammed Zeeshan Email: reach2zeeshan@gmail.com GitHub: YourUsername

Code Quality To maintain high code quality, please adhere to the following guidelines:

Comments and Documentation Docstrings: Include docstrings for all modules, classes, and functions following the Google Python Style Guide. Inline Comments: Use inline comments to explain complex logic or important sections of code. Code Formatting PEP 8 Compliance: Ensure all code follows PEP 8 style guidelines. Linters: Use tools like flake8 or pylint to check for style issues. Naming Conventions: Use meaningful variable and function names that convey purpose. Modularization Functions and Classes: Break down large code blocks into reusable functions or classes. Single Responsibility: Each function or class should have a single responsibility. File Structure: Organize code into modules and packages logically. Issues and Pull Requests We encourage community involvement to improve this project.

Issues: Use the Issues tab to report bugs or request features.

Pull Requests: Contributions can be made through pull requests. Please ensure your PR:

Is associated with an issue or describes the problem it's solving. Passes all existing tests and includes new tests if applicable. Follows the code quality guidelines mentioned above.