The Exchanges

Q if I'm a technology Personally, the company and I like data, data problems. How do I figure out what I use for different problems, right? So you have, you have key value stores, you have, uh, document databases, you have relational databases, you have graph databases. How does that, how does, how does that, or how do I choose the right tool? Uh, and how does it all work together?

A Yeah, so it's, it's actually pretty simple, right? You start with the shape of the data, and you look at the query workloads that you want to run in that data, right? And so if that data is very tabular, if it's a payroll system, and you want to record all the individuals, and they're all well structured, all of them have exactly the same schema, right? And you want to calculate average salary, and blah, blah, blah, stuff like that. Awesome. Relational database. Go, right? Or if you have a bunch of JSON documents sitting around and you don't really care how they're connected, right? Document database, go, right? Or if you have a data set that is highly complex, that is evolving, where the business requirements change, where the values and how things fit together, like a shopping cart, which is connected to order items, those order items are connected to product, which sits in a product hierarchy, and how things fit together, a graph database is your best bet, right? And so, so that ends up being kind of the The first go to move, look at the shape of the data, and then the queries you want to run on that, and that'll clue you in very rapidly, kind of where you should try to evaluate first.

AI assessment note: “You start with the shape of the data, and you look at the query workloads”

Q very much cover where I was going to go next. So let's use those. So, uh, first question from Balaji, um, was a couple of questions. I'll start with the first one. Uh, he says, Excuse me. There's been a flood of investments in the graph DB space. Uh, how does Neo for J difference differentiate itself? And more broadly, is there opportunity for more than one player to exist?

A It's a great question, right? So, so a couple of things on that in terms of differentiation, right? So we're kind of the OG graph database, right? We've been around the longest, and, and for, if you attend data-driven New York, you, you're probably somewhat clueful about, by data, so you'll know that in, in many product categories, you kind of want to be the new kid in, in, in many ways, right? For a database, maturity, robustness, stability, Is actually a key part of the value proposition. So the fact that we've been around, we were the, the OG, the one that defined it, and so on and so forth, is actually a massive advantage, because what this means is that we have by far the most robust product, by far the biggest developer community, and by far the biggest reference account base, like, so most, um, customers by far of all graph databases out there, right? We've also happened to have this modern Uh, which maybe sounds a little weird, like this native graph architecture, where a lot of the more recent, as, as the graph spaces become harder and harder, the more recent entrants, what they try to do is they try to layer graph functionality on top of their existing core, right? So they don't take the native approach, which takes forever to build, right? But that's ultimately the only way to get to the, to the scalability and the performance. So that kind of speaks to the first que…

AI assessment note: “In terms of differentiation... we're kind of the OG graph database”

Q Great. Can you give us a sense for, um, how many, how many are in the company right now? Like how, how big a company is it as an organization?

A Yeah, so, so we're just north of 600 people. Uh, I have no idea how many we were back in 2015. We actually just, earlier today, we went out with a, with a momentum release where we talked about how we crossed a hundred million ARR, you know, in, you know, last year. Um, and just, just to give a flavor, I think there's, there's five database companies, uh, that have crossed a hundred million, right, of the, kind of the, let's call it the, the NoSQL crowd, or like modern operational database companies. It's, You know, Mongo, and then it's us and Redis. We're on that kind of Mongo path. And then there's Couchbase and data stacks for, um, you know, maybe on a, you know, have been kind of traditionally on maybe on a little bit of a different path right now, and they, um, are growing maybe at a slower pace and plateauing, right? And maybe they'll turn around and become kind of amazing back again, right? But, but it's really down to kind of Mongo and us and, and, and Redis who's kind of in that, in that, in that cohort at the moment.

AI assessment note: “we're just north of 600 people.”

Q space, um, accelerating, going from, from niche to much broader, uh, acceptance? So I've, uh, I've seen that chart, that famous chart on DB engines where, uh, which of that graph databases is like by far the fastest growing, um, you know, category in, in, in databases. And, um, I read somewhere that Gartner calls graph databases, the, the foundation of modern data and analytics. Uh, so what's, what's happening?

A Yeah, I mean, look, there's, there's a lot of factors that I think are contributing to and accelerating and enabling the broader shift towards an alternative databases, right, that aren't specific to graph databases, things like kind of the platform shift to the cloud, and then there's, like, advancements in architecture like microservices and containers that enable you to more easily swap in a new type of database. Stuff like that, that is as applicable to any database as to graph databases. The thing that's specific to us is this broader trend around the world is becoming increasingly connected. And the fundamental premise behind what we do is, is super simple, actually. In fact, today people might even call it simplistic, right? Which is what I just said. Everything is increasingly connected. Hardly a controversial statement on a Zoom call, you know, from New York. I mean, Malmo, Sweden. Right now, a bunch of people are, I'm sure, calling in from New York, but also elsewhere, probably on the, you know, in, in, in, in the planet, right? And so everything is becoming more connected. We all know that intuitively, right? But kind of the consequence of that, that is a little bit more subtle, is that data, what is data? This is data-driven New York, right? What is data? Well, data, information, describes the real world. So as the real world is becoming more connected, data is beco…

AI assessment note: “this broader trend around the world is becoming increasingly connected.”

Q may not have any budget to buy your product. At what point did you switch, um, you know, targeting the larger enterprises? Like, at what point, I guess, did you get a sense that this was working, uh, and what did you do? Did you, uh, build the sales force to go up to the larger enterprises? Like, at what point do you go from bottoms up to tops down?

A So yeah, yeah, I was gonna say if ever, right? Like, so, so on some level we had a bifurcated approach, right? Where we, where we built the community, right? And that was the long-term focus and the right thing to do and so on and so forth, right? But then we also went out and kind of hand-to-hand comment with enterprise sales. And we tried to identify kind of for these core use cases where people have a lot of connected data today, not where they will have connected data five years from now, because everything's becoming connected, but today, right? Which are really valuable inside of the enterprise, willing to charge hundreds of thousands of dollars, right? Pay hundreds of thousands of dollars, right? And then we tried to identify them. We knocked on doors through our own kind of personal network or our graph, as we like to call it, right? You know, and, and sell into that, right? But that was much more to kind of see the community, get some of those anchor lighthouse accounts and stuff like that, right? So we'd had a bifurcated approach like this in the early days. About five years ago, probably around the time for Data-Driven New York, at that point, we had shifted, so over 85% of our ARR back then, and still true today, originate with an individual practitioner. It used to be an individual developer, now it's an individual developer or a data scientist. Who founders used o…

AI assessment note: “About five years ago, probably around the time for Data-Driven New York”

Q a Neo for J user. You, you require people to, uh, use a different language called Cypher. Uh, and, uh, I'm just curious how that compares to SQL, which is really the language that sort of everybody knows for databases. Uh, is that, is that, um, so why is that a different language and, um, how steep is the learning curve? Uh, if you know, SQL to know, Uh, cipher.

A Yeah, so, so the big comparison is probably something like the following. SQL is old and boring. Cypher is new and sexy. Done. That's it. Um, no, so it's, it's actually very spiritually, very similar, right? It's a declarative query language, which basically means that you don't have the right programming language, imperative code, depending on how technical the audience is, right? Um, but you can type it in, in a very simple, you can describe what pattern you're looking for, right? And, and you draw it and Some of the people who, like, are older in the audience will, will recall this, right? With something called ASCII art, which is basically you end up drawing, like you, you draw notes using parentheses, and then with arrows, you kind of can describe the little pattern, and then you throw that to the graph database, and it's going to find that pattern and return it back to you, right? So spiritually very similar to SQL, but the really Pretty astounding. One of the biggest things that have happened since, since the, since 2015, we, it's probably a good thing for us to contrast to, to, to what it was like last time we spoke, right? Is that, um, Cypher is the most popular graph database query language. Um, but what we've ended up doing is that we went to the SQL committee. So the committee that is standardizing SQL, right? And we said, you know what? We don't want Cypher to be p…

AI assessment note: “it's actually very spiritually, very similar, right? It's a declarative query language”