Data modeling

Let’s review together three unique challenges around data modeling that are specific to non-relational databases.

Denormalization

In non-relational databases, it’s common to use denormalized data models where data is duplicated across multiple documents or collections. This is done to improve query performance and avoid expensive joins. In contrast, relational databases emphasize normalization, where data is organized into separate tables to avoid duplication and maintain data integrity.

Denormalization can introduce unique challenges around data consistency and update anomalies. When data is denormalized, it can lead to redundant or inconsistent data, which can be difficult to manage. For example, if a customer’s address is stored in multiple documents, updating the address in one document may not propagate to all the other documents, leading to inconsistent data.

Here’s an example of a denormalized data model in MongoDB:

MongoDB...