This project explores the design of relational data models for real-world systems. The focus is on translating business requirements into well-structured database schemas that are efficient, consistent, and scalable.
Poor data modeling leads to inefficient queries, data inconsistency, and long-term scalability issues. This project explores how to design relational schemas that accurately represent domain requirements while balancing normalization, performance, and maintainability.
- Translate business requirements into relational schemas
- Understand normalization (1NF, 2NF, 3NF) and when to denormalize
- Design entity relationships (one-to-one, one-to-many, many-to-many)
- Apply primary keys, foreign keys, and constraints effectively
- Model real-world domains using relational design
- Understand schema evolution and migration concerns
- Explore tradeoffs between flexibility and performance
- Entity-relationship modeling (ER diagrams)
- Table design fundamentals
- Primary and foreign key design
- Constraints and uniqueness rules
- One-to-one relationships
- One-to-many relationships
- Many-to-many relationships (join tables)
- Hierarchical data modeling
- First Normal Form (1NF)
- Second Normal Form (2NF)
- Third Normal Form (3NF)
- Denormalization strategies
- User and authentication systems
- E-commerce systems
- Inventory and order systems
- Notification systems
- Audit and logging systems
- Data integrity and consistency
- Query efficiency vs schema normalization
- Future schema evolution and migrations
- Indexing implications of schema design
- Real-world constraints and business rules
- PostgreSQL (primary implementation database)
- ETL pipeline (data movement and transformation)
- Backend services (API-driven data access)
- Query optimization projects (performance tuning)
🟡 Planned