This always confuses me because we have decades of SQL and all its issues as well. Hundreds of experienced devs talking about all the issues in SQL and the quirks of queries when your data is not trivial.

One would think that for a startup of sorts, where things changes fast and are unpredictable, NoSQL is the correct answer. And when things are stable and the shape of entities are known, going for SQL becomes a natural path.

There is also cases for having both, and there is cases for graph-oriented databases or even columnar-oriented ones such as duckdb.

Seems to me, with my very limited experience of course, everything leads to same boring fundamental issue: Rarely the issue lays on infrastructure, and is mostly bad design decisions and poor domain knowledge. Realistic, how many times the bottleneck is indeed the type of database versus the quality of the code and the imlementation of the system design?

> end up re-implementing relational databases at the application level anyway

This is by design, the idea is that scaling your application layer is easy but scaling your storage/db layer is not

Hence make the storage dumb and have the application do the joins and now your app scales right up

(But tbh I agree a lot of applications don’t reach the scale required to benefit from this)

I've never used DynamoDB in production, but it always struck me as the type of thing where you'd want to start with a typical relational database, and only transition the critical read/write paths when you get to massive scale and have a very good understanding of your data access patterns.

Same. DynamoDB is almost never a good default choice unless you've thought very carefully about your current and future use cases. That's not to say it's always bad! At previous startups we did some amazing things with Dynamo.