How do you design a database schema that is flexible and adaptable to changing requirements?

When designing a database schema that is flexible and adaptable, there are several key principles to keep in mind:

• Normalization: Normalizing your database schema reduces redundancy and ensures data integrity. By organizing data into separate tables and defining relationships between them, you can easily update and modify your schema without impacting the entire database structure.
• Denormalization: While normalization is important, denormalization can also be useful in certain scenarios. Denormalization involves adding redundant data to improve query performance. However, it’s essential to strike a balance between normalization and denormalization to maintain data consistency.
• Use of Indexes: Indexes help speed up query performance by allowing the database management system to quickly locate data. By properly indexing your database tables, you can ensure that your schema remains flexible while still meeting performance requirements.
• Version Control: Implementing version control for your database schema enables you to track changes over time. By maintaining a history of schema modifications, you can easily revert to previous versions if needed.
• Flexible Data Modeling: When designing your database schema, consider using a flexible data modeling approach like Entity-Attribute-Value (EAV) or JSON columns. These techniques allow you to store varied data types within a single table, making your schema more adaptable to changing requirements.