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This seems like reasonable discussion, but you would get far more traction if you have an opportunity to write an entire schema from scratch in the proper way.

Not having to fight assumptions along lines of improperly denormalized columns (i.e. which table is source of truth for a specific fact) can auto-magically simplify a lot of really horrible joins and other SQL hack-arounds that otherwise wouldn't be necessary. The essential vs accidental complexity battle begins right here with domain modeling.

You should be seeking something around 3rd normal form when developing a SQL schema for any arbitrary problem domain. Worry about performance after it's actually slow. A business expert who understands basic SQL should be able to look at and understand what every fact & relation table in your schema are for. They might even be able to help confirm the correctness of business logic throughout or even author some of it themselves. SQL can be an extremely powerful contract between the technology wizards and the business people.

More along lines of the original topic - I would strongly advocate for views in cases where repetitive, complex queries are being made throughout the application. These serve as single points of reference for a particular projection of facts and can dramatically simplify downstream queries.



One of the strengths of Metabase is that it can plug into a variety of data sources, not just RDBMS. For example, AWS Athena over data in S3 buckets. Good design can still make things easier, of course, but not always an option. Relational purity is not going to be an option in such circumstances, so are in my opinion correctly not addressed in the piece.


Metabase sounds like a great tool for building clean analytic schemas then. You still need to design those schemas though.




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