Databricks can support Third Normal Form (3NF) data models, particularly within a data warehouse’s “Silver” layer, where data is curated and transformed. However, due to potential performance challenges, Databricks does not strongly recommend 3NF for new data models.

Here’s a summary of 3NF in the context of Databricks:

What is 3NF?

The third Normal Form is a database normalization technique that eliminates redundancy and ensures data consistency. In a 3NF model, each non-key attribute depends only on the primary key, not other non-key attributes.

Databricks and 3NF

• Databricks is capable of handling 3NF models, especially when you leverage Unity Catalog for metadata management and define primary and foreign key constraints. It’s important to note that heavily normalized models like 3NF can lead to numerous joins in queries, potentially impacting query performance on Databricks.Alternative Recommendations: Databricks often suggests denormalized or partially normalized models like the star schema or snowflake schema for better query performance.

When to Consider 3NF on Databricks

• Existing 3NF Data: If you have existing 3NF data that you need to migrate or query on Databricks, you might choose to keep the existing structure initially and evaluate performance before making changes.
• Specific Use Cases: In some scenarios, the benefits of data consistency and reduced redundancy in a 3NF model might outweigh the potential performance concerns.