AI, SQL, and the End of Big Data

**Jordan Tigani** (0:00)
One of the first times that I really realized that like, hey, LLMs are actually changing the way people are using data is when we had not launched too long ago, and you were using MotherDuck, and you mentioned that you were kind of cut and pasting between ChatGPT and our query UI.

**Jennifer Li** (0:15)
It's the mission of like a very raw MS SQL skills.

**Jordan Tigani** (0:19)
No, I think it's the kind of thing where everybody forgets syntax for various SQL calls, and it's just like encoding us. So there's some people that memorize all of the codebase, and so they don't need auto-complete. They don't need any co-pilot. They don't need an IDE. They can just type in notepad. But for kind of the rest of us, I think these tools are super useful.

**Derrick Harris** (0:41)
Welcome again to the a16z AI Podcast. I'm Derek Harris, and joining me this week are a16z general partner, Jennifer Lee, and MotherDuck co-founder and CEO, Jordan Tigani. If you're not familiar with Jordan and MotherDuck, the short version is that MotherDuck is a commercial database offering built on a very popular DuckDB open source project. Among Jordan's past accomplishments was building BigQuery while at Google, and he's also presently the unofficial spokesperson for the small data movement. In this episode, we discuss DuckDB and the move away from all big data all the time, as well as where database technologies are slotting into the stack for production AI applications, for things like vector search and more. Jordan also touches on the future of text to SQL and the importance of clean data if you want to sick an LLM on it. Also of note, MotherDuck is hosting the Small Data SF conference on September 24th in, you guessed it, San Francisco, and we have registration information in the show notes. With that out of the way, get ready to hear Jordan, Jennifer, and myself talk about data.
As a reminder, please note that the content here is for informational purposes only, should not be taken as legal, business, tax, or investment advice, or be used to evaluate any investment or security, and is not directed at any investors or potential investors in any A16z fund. For more details, please see a16z.com/disclosures.

**Jordan Tigani** (2:10)
I kind of encountered DuckDB, this sort of upstart database, when it started showing up in these benchmarking reports. And so I was working at Single Store, and we kind of prided ourselves on performance. And all of a sudden, we were being compared against this just sort of out of the blue database had been created by a couple of academics in Amsterdam. And I started poking at it a little bit, and it's like, this seems really interesting. You can do stuff that we wanted to do when I was in Google, we wanted to do when I was at Single Store, and things that customers were asking for in terms of how it's scaled, and could scale down, and do things incredibly low latency.
Somebody should probably take this and build a SaaS service around it, and put it in the cloud. Well, maybe that's somebody should be me. I've built two database SaaS services, and as I started thinking about it, I reached out and I talked to the DuckDB co-founders, and wanted to see if they were going to do something similar to this. And they said no, they said they really want to just focus on kind of building the core database, but they thought it was a good idea, and they said, we'd love to partner with you, and sort of with your background, it would be great to work with you. And then I was just off and running. So we raised money pretty quickly, put together a team around the idea, pretty quickly coalesced around sort of what we were building, and the idea that we kind of had this extraordinary database, we could put this in the cloud, it was going to move really quickly, we could do low latency stuff, it could be a data warehouse, it could also work in a lot of the other ways that DuckDB could work, which was for data science use cases, and then sort of in a lot of these sort of nooks and crannies. And the other thing that really kind of struck me about DuckDB was the way they focused on sort of what database users actually care about. So a lot of times, like, I worked on databases and we spent all this time focusing on improving the time from when we as the database get a query and to the time that we have finished that query. But that's not actually the time that's experienced by users. I mean, first of all, there's the time it takes to get the query and to get the results back, but also there's paging the results. So that's just the actual query mechanics. And then you step back and there's sort of like, I have a problem that I want to solve. I formulate that problem. I have an idea that I want. I have a business problem. I want to understand something about my data. What's the time until I get my answer? And that was actually something that the DuckDB team has done a great job of actually solving. A good example of that would be the CSV parser in DuckDB. CSV parser is something like in BigQuery, it was like an afterthought. We had a college hire work on this, a new grad, and they were great, a great engineer, but sort of like, okay, we've got CSV parsing done, all right, we'll move on to the next thing. And DuckDB has a full-time person working on this. They've written research papers about it. But I would say this is the best CSV parser in the world. And the advantage for the user is, I just pointed to CSV file, it doesn't matter if it's got wonky things in it, errors or has weird separators or weird null characters. There's all sorts of ways that CSV files can be messed up. But the effect of it for a user is, hey, it just works. I can just query against it, and it works and it's fast, and it runs in parallel, and I don't have to worry about anything. And to me, that was really captivating because I feel like it's just an area that the whole database community was really missing the boat on, and that there was an opportunity to just make something easier to use and make something more user-focused.