@rabbitturtles , if one of the objectives you have is to enable your business users to take advantage of AI in Databricks platform, there's value in having the Gold tables in Databricks. You could create Genie spaces, for instance, that only interact with the gold tables that you provide it access to. Genie spaces would be one of the many AI related use cases, of course.

The denormalised table doesn't sound like a bad idea in my opinion. Part of data modelling is to enable the business users to be able to interact with the model, if this suits your business needs, then it's worth at least trying it out. You could always run a type of "A/B" test and see how the users interact with both forms of the model. You could also benchmark the effiencies/cost and trial out things like altering the table i.e. datatypes, column names, removing/adding columns. Which ones cause the biggest impact to you. 

On a slightly different note, thinking longer term about your table, and the size it may be, you could think further ahead about partitioning/liquid clustering: https://docs.databricks.com/aws/en/delta/clustering  https://docs.databricks.com/aws/en/tables/partitions .. this could be quite cool in terms of query performance and cost savings if you know common query patterns that the team would use.

All the best,
BS