An alternative approach is to leverage database indexing. By defining appropriate indexes on the columns frequently used for filtering or ordering data, you can significantly speed up query execution. Python's database libraries allow for creating and managing indexes programmatically, contributing to improved performance.

One way to optimize data retrieval from relational databases using Python is by utilizing database connection pooling. Instead of establishing a new connection to the database for each request, connection pooling allows for reusing existing connections, reducing the overhead and improving performance.

Another way is to use SQLAlchemy, a popular Python library that provides a high-level Object-Relational Mapping (ORM) interface. With SQLAlchemy, you can write Python code to interact with the database, abstracting away the underlying SQL queries. It offers features like lazy loading, query optimization, and caching to enhance data retrieval efficiency.