Obviously a lot of this you can piece together today, in fact Snowflake itself does a lot of it. But the other part of the article makes me think they understand the even harder part of the problem in modern enterprises, which is that nobody has a clear view of the model they're operating under, and how it interacts with parts of the business. It takes insane foresight and discipline to keep these things coherent, and the moment you are trying to integrate new acquisitions with different models you're in a world of pain. If you can create a layer to make all of this explicit - the models, the responsibilities, the interactions, and the incompatibilities that may already exist, then mediate the chaos with some sort of AI handholding layer (because domain experts and disciplined engineers aren't always going to be around to resolve ambiguities), then you can solve both a huge technical problem but a much more complicated ecological one.

Anyway, whatever they're working on, I think this is the exact area you should focus on if you want to transform modern enterprise data stacks. Throwing AI at existing heterogenous systems and complex tech stacks might work, but building from scratch on a system that enforces cohesion while maintaining agility feels like it's going to win out in the end. Excited to see what they come up with!

Also, obviously it's your life, and we're here on Earth to fart around, but I have spent a good portion of my life dipping into one hobby after another, as my dad did before me, so I'm half speaking to myself when I ask this: why do you think you can't meaningfully contribute to any of these realms, even now? To me that sounds like some deep seated fear or doubt, some aversion to competition, some overriding bitterness. I'm slightly worried you'll just be back here in another couple of years trying to find another new hobby, unsullied by the efforts and achievements of others. You won't find that! I would actually suggest a particularly expensive hobby: going to therapy. Try that, and learn that you're already enough, and if your contributions are meaningful to you, that's all that matters. Happy to be way off the mark here though.

All that said, 90% of the time you still just eval a bit of a code to see what happens and that's the same between the two languages.

https://github.com/BetterThanTomorrow/calva-backseat-driver