Yoda Response: Certainly, Avi. To design a system for managing and processing large datasets in Python, ensuring both efficiency and scalability, I would take the following steps:

1. **Data Storage**: Use a robust database system like PostgreSQL or MongoDB to store the datasets. These databases are designed to handle large volumes of data efficiently.

2. **Data Processing**: Utilize libraries like Pandas for data manipulation and processing. Pandas provides powerful data structures and functions to handle large datasets efficiently.

3. **API Design**: Implement a RESTful API using Flask or FastAPI to allow the front-end to interact with the backend seamlessly. FastAPI, in particular, is known for its high performance and ease of use.

4. **Batch Processing**: For very large datasets, implement batch processing using tools like Dask or Apache Spark. These tools can distribute the data processing tasks across multiple cores or machines, ensuring scalability.

5. **Caching**: Use caching mechanisms like Redis to store frequently accessed data, reducing the load on the database and improving response times.

6. **Optimization**: Optimize the code for performance by profiling and identifying bottlenecks. Use efficient data structures and algorithms to minimize memory usage and processing time.

For example, during my internship, I developed a machine learning preprocessing CLI tool that handled large datasets. I used Pandas for data manipulation and AWS EC2 for scalable processing power. By following these principles, I ensured the system was both efficient and scalable.