SQL

“The top, the very top, SQL, is aspirational. We do intend to do it but we haven't started working on it yet.”

“Oftentimes, ah, from a question perspective, the interesting insights in the data are actually found outside of your selection, ah, outside of your query.”

“Trained almost the entire company to use SQL.”

“So, you know, Cockroach bridges the gap. It's both of those things. It has both of those things, and it doesn't compromise.”

“They're gonna need SQL skills, they're gonna, they might need Python or R skills, they'll need something like Excel but those things are learnable, whereas I think, like, the way of thinking is often harder to teach.”

“Me knowing SQL and me knowing how to create a chart doesn't make me an analyst any more than knowing Photoshop makes me a designer.”

“Instead of depending on foreign keys to relate the data, you can actually just use time and customer.”

“What's happening now or over the past, you know, five or so years, the, Transformation step now happens inside the data warehouse and it happens in SQL. And because of that, it is now accessible to a dramatically larger number of people.”

“Any pitch. I'm generally very skeptical where you can tell folks, you can have all these great new benefits of low latency or whatever it is, but you got to start all over from scratch, right? You have to throw everything out there and you're going to rebuild …”

“The other one that I, I'd point to that I haven't used deeply myself, but I just think is a thing that the world really needs is SQL fluff. SQL FL U F F. And it's a linter for SQL. And I'm just kind of shocked that we have got to 20 21 without there being a, l…”

“We really do think that the future of data processing is polyglot, and I think that if you look in five years, you will find more robust abstractions on top of data, and even in the DBT ecosystem, than SQL.”

“There are actually two orders of magnitude more human beings on the planet that can write SQL than can write Spark or Scala or whatever.”

“They're moving in, like, the semantic layer tool, so basically they're saying, give us raw data, and we will tell you, like, apply semantics to it. The way that they do that now is through SQL. So, like, semantics are kind of, like, air quote semantics. It's b…”

“The way this problem is solved in most enterprises is that you deploy armies of analysts, and they get tickets and requests, and they write SQL, right?”

“So the data stack is, in my team, the data stack is SQL and scikit, and occasionally Go”

“Trying to put together SQL queries in such a manner to understand those interrelationships is virtually impossible, and honestly was part of the reason that you don't see a lot of the value derived when you buy the data, because you have to spend so much time,…”

“You can write a parquet object and then query it using SQL natively off of our platform without needing any higher layers on top.”

“The next one I said SQL isn't going anywhere, and I kind of said that for more than one reason. One, I feel like someone's going to tell us it's time to revamp data lake v one again, where we'll just dump everything in there and we'll let an LLM figure it out …”

“SQL you can learn quickly. What's going to take time is just getting a sense for how data Operates and flows and data sets in general.”

“So the idea that I'm supposed to adopt a SQL semantic concept into my natural language is the ultimate hubris of engineering.”