Artie: Real Time Data Streaming For The AI Age

1. JFJared Friedman0:00

   [upbeat music] Really excited to be sitting down today with the founders of Artie, Robin and Jacqueline, who just announced that they've raised a Series A. Congratulations on the Series A, guys, and thanks for joining us.

2. JCJacqueline Cheong0:15

   Yeah.

3. RTRobin Tang0:15

   Thank you.

4. JCJacqueline Cheong0:16

   Thanks so much.

5. JFJared Friedman0:16

   To start with, do you wanna just tell everybody what Artie is, and perhaps you can tell everyone about the Series A you guys just raised?

6. JCJacqueline Cheong0:22

   Yeah. So Artie is a real-time data streaming platform. Um, what that means is, we help companies move data across their systems in real time. So imagine you have production data in Postgres, we will stream it over as changes happen into, you know, a Snowflake, as things are changing under the hood. We just raised a 12 million Series A from Dalton Caldwell, Paul Buchheit, and Brian Berg from Standard Capital, and we're really excited to be chatting today.

7. JFJared Friedman0:50

   Let's rewind back to the beginning. You guys were in Summer '23. Maybe tell us a bit about, like, how this got started and how you ended up working on this.

8. RTRobin Tang1:00

   Yeah. So this all started with my background where, like, d- depending on the various companies and roles I was at, we were either using, like, you know, Databricks or BigQuery and what have you, and the same thing kept popping up. I would be asking my data team pe- to ask for faster and fresher data. When I was working on growth at Opendoor, I wanted faster data so I can experiment, do faster experimentation, or I wanted to operationalize certain use cases using, like, a tool like Retool. And I was basically always told some variant of, like, "This is too hard for my data team," or, like, "We don't have the capability or resourcing to build this, so, like, unless it's a company P0, don't, don't, don't bother." So then I tried to buy a tool. Then I discovered, you know, the managed batch players out there and found that they were, like, really good at getting set up, really good for SaaS data sources, like HubSpot and Zendesk, but completely different buyer persona, right? Where, like, you know, the SaaS data sources, they're more geared towards business users or, like, marketing users, and what I wanted was a production database, and that's more geared to our, like, infra and platform engineers. And because of the fact that, like, they just weren't able to keep up with the load, we ended up having to try to build something in-house. I had a team try to build this for about a year, and by the end of it, it wasn't production-ready. So then I pulled the plug and then started to ask questions around, like, at... Every company at a certain data scale needs to build a tool like this to be able to use some sort of, like, a change data capture mechanism, and just spending a year to two years building a Postgres to Snowflake connector just seems weird. Like, it seems nonsensical.

9. JFJared Friedman2:29

   Yeah. It seems like not anyone's else's core competency.

10. RTRobin Tang2:31

    Exactly.

11. JFJared Friedman2:32

    Like, you should be focused on your product, not m- not building, like, a weird data connector.

12. RTRobin Tang2:36

    Yeah. And then that's when I was like, "Okay. Well, if it doesn't exist, let me just build it myself," and that's what, that's all, that's, that's ultimately what started Artie.

13. JFJared Friedman2:44

    What are the companies where you saw this problem?

14. RTRobin Tang2:46

    When I was at Zendesk, we ended up rebuilding, like, our enterprise data warehouse a few times. We were already using change data capture, so Zendesk open sourced this tool called Maxwell. We were trying to use that for, like, data integrations, and just doing that alone was still too hard. Then I joined Opendoor, Postgres to Snowflake, and again had the same problem. We were trying to build something like this but just didn't have the capabilities to really build it.

15. JFJared Friedman3:10

    When you tried to build this before Artie at-

16. RTRobin Tang3:12

    Mm-hmm

17. JFJared Friedman3:12

    ... these previous companies, engineers spent, like, a whole year-

18. RTRobin Tang3:16

    Mm-hmm

19. JFJared Friedman3:16

    ... trying to build this and, like, failed to build it.

20. RTRobin Tang3:18

    Mm-hmm.

21. JFJared Friedman3:20

    How long did it take you to build it at, like, at, at, as Artie? [laughs]

22. RTRobin Tang3:24

    Okay. Before AI got good, we w-

23. JFJared Friedman3:27

    [laughs]

24. RTRobin Tang3:27

    When we started this, it took us about six months to build this. Now, if I, if I had to, like, th- really, if I, if I had to do it, redo again, probably would take, like, two to three months.

25. JFJared Friedman3:35

    Wow.

26. RTRobin Tang3:35

    But did take six months because it was, it is a hard problem.

27. JFJared Friedman3:38

    Yeah.

28. RTRobin Tang3:38

    And, you know, during the batch, it was funny because we didn't fully make our product, like, fully self-serve yet. We onboarded our first customers using Google Sheet and, like, tracking backfill progress using that.

29. JFJared Friedman3:49

    Yeah.

30. RTRobin Tang3:49

    And then during the YC batch, we made it so that, like, it appeared self-serve so that customers can onboard tables, but what it would do ultimately was send me a Slack ping to be like, "Hey, go onboard this table." And it w- it's funny because we had a customer in New York. They onboard at 8:00 a.m., so it's, like, 5:00 a.m. for us, and then they're like, "Wait, it's been stuck for a couple hours." By the time I woke up, I saw, I'm like, "Oh, [laughs] I need to, I need to kick off a backfill for this." And then-

Episode duration: 26:33