Phase 2: Database Development

To facilitate more efficient data collection and trend forecasting by academic term, it was created a SQL database using:

• QuickDBD: For database diagram creation and relationship mapping

• MySQL Workbench: For database design and query development

• Google Cloud: For database hosting and accessibility

• Visual Studio with Python: Data processing pipeline to convert Excel data to JSON format with privacy protection measures

Database design followed normalization principles to minimize redundancy while maintaining the system's reporting capabilities.

Database Architecture Documentation: