Earlier we discussed the silver layer, where we mainly write data cleansing logic using PySpark. I'm curious: would you still use Databricks notebooks and PySpark commands to create the gold layer? Are you familiar with dbt and can you execute dbt using traditional SQL templates?

Yes, the gold layer can be built with either PySpark or dbt, depending on the team’s skill set and the complexity of transformations. PySpark in Databricks notebooks is great for iterative, programmatic data cleaning and complex logic that benefits from Spark’s distributed processing. dbt, on the other hand, excels at declarative SQL transformations, version control, and testing. In a hybrid approach, I would use PySpark for heavy‑lifting tasks (e.g., large‑scale joins, UDFs) and then load the cleaned data into Delta tables. After that, I would use dbt to model the gold layer: write SQL models, define relationships, and enforce tests. dbt can run on Databricks by pointing it to the Databricks SQL warehouse or using the Databricks CLI. This combination gives us the best of both worlds: scalable processing and maintainable, testable transformations.