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For Mongo I write a data layer that has methods like GetFoo() or UpdateFoo() where the methods have implicit knowledge of key structure (and query model objects for validation, etc)

For SQL I would do something similar but use (for example) SQLAlchemy Core (not ORM) to do DB-agnostic SQL generation. That would essentially give me a SQL API somewhat similar to pymongo.

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For some things ORM's get in the way, but avoiding them by your own data layer sounds like a pretty big time-suck.

Being able to write a single line of SQLAlchemy to build relationships into your models seems way simpler than implementing relationships in a custom data layer.

Yeah, I think the sweet spot is using SQLAlchemy's ORM for simple queries and dropping down to core for more advanced usage. Best of both worlds.

Even for advanced queries (ok, not super advanced) SQLAlchemy can hold its own, especially if you're willing to dip into the sqlalchemy.core modules.

I don't deny that it's more work. The question is whether it's worth the investment. I think for large, complex codebases (not little one-off apps) the gains in performance and long-term maintainability are worth it. Having the ability to know the exact performance implications of a query (and not having to waste brain cycles on questions about lazy vs eager loading, etc) is more desirable.

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