I have a pet theory that any given system has a maximum cleverness budget. Make sure to spend it on the things which actually matter in your problem domain.

Interesting data products often have to spend a lot of that cleverness budget in the algorithmic layer – machine learning and statistics. So when I work on that kind of thing, I've learned to have a real appreciation for boring data storage. I'll even happily use MySQL; the ways in which MySQL can suck are very well-understood and that's a hugely underappreciated virtue.

This 'cleverness budget' is a fantastic term. Thank you!

I've never worked with postgres before my current project and I'm so impressed with how easy it is to get stuff done. I used Cassandra for a prototype (bad choice for my workload, not Cassandra's fault) and documentation, ease of tuning, tooling maintenance all seem to be so much easier. Of course, Cassandra has a host of problems to solve that postgres doesn't -- being meant to run on clusters and all. But you know what, I don't care. I also was heavily leaning new stuff before. But what can I say, if an RDBMS is what I need -- I probably should use one.