What is the difference between data normalization and denormalization?

What is the difference between data normalization and denormalization?

Data normalization is the process of organizing data to reduce redundancy, while denormalization is the opposite, where data is combined to improve read performance.

Can you give me an example of when to use normalization?

Normalization is ideal for transactional systems where data integrity is crucial, like banking systems.

And when would I use denormalization?

Denormalization is often used in data warehousing or reporting systems where read performance is prioritized over write performance.

What are the pros and cons of each approach?

Normalization reduces data redundancy and improves integrity, but can slow down read operations. Denormalization speeds up reads but can lead to data anomalies.

How do I decide which method to use for my project?

Consider your project's requirements: if data integrity and consistency are key, go for normalization; if performance is critical, consider denormalization.