Business Breakdowns
· Matt Reustle
[00:00:00] S0: Yeah, it's very interesting to hear. And we had discussions before this conversation about your involvement in the business, and I'm very interested in late-stage investors and private businesses and what insights they glean. And when you painted that picture to me of that evolution into a true platform, it checked out to me just in terms of, "Okay, this was a unique moment. They've evolved." On that point about competition, at the highest level, if I'm an organization using Databricks, am I potentially also using Snowflake? Am I using multiples? Is it not necessarily winner-takes-all market? But how does that work just in terms of how much dominance there is with customers when they choose one versus the other?
· Alan Tu, Matt Reustle
[00:00:00] S1: The reality of the market is that there tends to be multiple vendors that enterprises will use. And actually, I think Databricks has contributed towards a trend of enabling more types of tools for more types of workloads. Now, Databricks we believe will then be able to come out with more products to address those different workloads. But to answer your question, it is very much the case that you see customers using both Databricks and Snowflake, for example. If you roll back the clock to that core use case that Databricks addressed early on around data processing, it's actually a very classic situation where an enterprise might use Databricks first to process the data and then store that data in a Snowflake data warehouse. Snowflake has started to try to move upstream to do more data processing, and Databricks has moved downstream to do more of the data warehouse. But that can give you a sense of the way that these tools can live together within the same company. [00:00:00] S0: I will acknowledge, this is very easy for me to say from the cheap seats, but it would seem as though moving from the processing, the unstructured world, into the warehousing, more structured world, might be a smoother evolution than vice versa. Because in my mind, that unstructured world is a very complicated solution that was being offered, and not that data warehousing is not, but do you think that's a fair representation? And definitely feel free to push back on that assumption.
· Alan Tu
[00:00:00] S1: Well, I would say that empirically, if you look at the numbers, that has played out to be true with the data warehouse product scaling to a billion dollars. That's dwarfed the analogous revenues that Snowflake has had around moving to data engineering. But what I would say is that, not being able to run an A/B test in different versions of the world, I would say that there was a lot of Databricks execution that led to them having more success moving towards structured. We touched on it briefly earlier, but I thought this was another example of the company being not just great technologists, but actually really good, savvy marketers and folks with a commercial gut instinct. When they recognized that they wanted to move into structured, and there was this concept that unstructured, that what people would call kind of that world would be data lakes, and then structured would be data warehouses. And so, Databricks actually came up with this terminology called the "Lakehouse," combining the data lake with the data warehouse. And at the time, you can go back to some of the news coverage, there was quite a bit of ridicule about this idea of Lakehouse. It was almost too clever. This idea, "Oh, you're going to combine the two, and you've come up with this name." You fast forward to today, and the Lakehouse is a very real, defined category that industry observers have all coalesced around. So, the credit that Databricks deserves, not only for executing on the product and the technology to achieve, again, that we've talked about with data warehouses, but to then do all the work to educate the market around why the Lakehouse architecture is the best of all worlds and why that is the future, is an incredible piece of the story that I think Databricks probably doesn't get enough credit for.
· Matt Reustle, Alan Tu
[00:00:00] S0: Yeah, I think those things are very hard to measure, but you certainly can appreciate them sometimes more after the fact. And I certainly just give companies bonus points when they're having fun while doing this execution. There's something that just seems to matter to me. It shows a willingness to enjoy the aspect of business and competition and whatnot. [00:00:00] S1: There's a certain amount of fun, and I don't know if they would use these words, but I feel like irreverence in terms of, I think this ties back to the founding heritage and DNA where it's, "Look, let's have an opinion about where the world is going." I think as an investor, if you go back to the early bets, they would tell you, "These are the three bets that we're making. We're making a bet that cloud will be big, data will be big, open source will be a good way to build a business, or at least build adoption." And here it was, "We think that the Lakehouse will be big, and we think that this is where the world should go. We think that this will help customers, and we are going to bet behind that." It just makes it very clear that if you're betting on Databricks, you're betting on this future state of the world. And I find that sometimes with companies that try to have it all, they say, "Oh, we'll be good here, we'll be good there." But the reality is that that detracts from your ability to execute in a way that Databricks did with something like the Lakehouse.
· Matt Reustle, Alan Tu
[00:00:00] S0: I think things like first principles can get thrown around a lot, but in preparation for this, watching, reading a lot of things that Alan has done in terms of interviews, it checks out in terms of the approach. And there's a certain clarity to the academic world and being born out of that in terms of understanding exactly what you're doing, that focus, having that clarity in terms of why you're going after things, which I think can sometimes get drowned out by some of the other baggage that comes with academia, but putting that aside. [00:00:00] S1: Well, I would also say that, I guess a different way of describing this is that Databricks is helping to lead the industry to where they think the industry should go. They identify where the pain points are, and they come up with a solution that they think makes sense, as opposed to looking at an existing market and just saying, "Okay, well we can do a 'me too' product just for the sake of expanding our TAM." What I've found is one of the underlying things that Databricks does a really good job of is recognizing true value creation as opposed to just monetizing and revenue growth, if that makes sense.
· Alan Tu, Matt Reustle
[00:00:00] S1: Well, I would also say that, I guess a different way of describing this is that Databricks is helping to lead the industry to where they think the industry should go. They identify where the pain points are, and they come up with a solution that they think makes sense, as opposed to looking at an existing market and just saying, "Okay, well we can do a 'me too' product just for the sake of expanding our TAM." What I've found is one of the underlying things that Databricks does a really good job of is recognizing true value creation as opposed to just monetizing and revenue growth, if that makes sense. [00:00:00] S0: It's based on customer challenges, but also having a predictive view in terms of what's going to happen in the future. There's a little Steve Jobs thing about designing for what the customer doesn't necessarily know what they need yet in there, not to draw too hard on analogies. On the point on market expansion, TAMs, all of that, when it comes to both Databricks and Snowflake, both are relatively young businesses. Were they replacing industry incumbents? Was it all new market creation? How would you describe the TAM that exists relative to what it was, whether it's prior to cloud or even within the cloud and using some of the incumbents?