Libertarians and mentally speed-running the invention of the nation-state: an iconic duo.

The individuals will get taxed on capital gains afaik. We could also tax unrealized gains (just like we do gains on property)!

IIRC these concepts are more relevant in a service-based economy.

In the Middle Ages, _land_ was the primary source of economic value. Fewer people means more land per person, therefore more wealth per person.

Land is still the primary source of economic value. And I don't think land was getting fairly allocated between all the peasants during the late 1300s.

It wasn't but you had the opposite of the effect that you have now. Right now the top 1% are getting a lot of the additional wealth that's being created, while the bottom 99% stay mostly the same, or improve at slower speeds. So yes, this means that we are headed towards a more inequal world, even though the situation improves for everyone.

Turn that now around, and kill a lot of people from the bottom 90% while letting the top 10% survive at higher rates. Suddenly, the world looks way more fair, even though the people at the bottom still have nothing.

What makes left joins perform poorly in Snowflake?

It has to join each part of the previous join to the next join, and if you have a lot of joins this can get out of hand.

We have a lot of joins in our final fct orders from our intermediate table, and looks like this:

from foo left join bar on bar.common_id = foo.common_id left join baz on baz.common_id = foo.common_id left join qux on qux.common_id = foo.common_id left join waldo on waldo.common_id = foo.common_id

So waldo joins to qux, which joins to qux... I call it a "staircase join", as that's what it looks like in the SF profiler.

Well part of the benefit is rapid development; it's mind-boggling how quickly someone can stand up a dbt project and begin to iterate on transforms. Using Python/SQL/JSON (at small/medium) scales keeps the data stack consistent and lowers the barrier to entry. No reason to prematurely optimize when your bottleneck is the modeling and not the actual data volume.

dbt and ELT in general are such a game-changer for allowing rapid iteration on business logic by data analysts; the feedback loop feels much more like "normal" software engineering compared to legacy systems.

When will an ORM be available?

Still not sure whether this is serious or not, but it's not really infrastructure as SQL, it's infrastructure as database records which is stateful and defeats the point.

Is this sarcasm? I have a broken sarcasm detector.

It's using an implied cross join so each image is deployed onto a t2.micro

Ahh, gotcha. I appreciate the response there as I wasn't aware of that notation and even then I can't think of any time I've used a cross join. Not sure which syntax I would use personally.

> I've used a cross join

They're good for getting rates on small datasets. think (select grouper, count(1) from data) cross join select count(1) from data) I think I've mostly used them in interviews, tbh.

IMO if you have to lie, you've already ceded the moral high ground.

tl;dr just ignore it?