In July 2021, Neo4j announced a large round of fundraising—its largest to date. Philip Rathle talked with Alan Shimel about how the capital will be used to further Neo4j’s go-to-market strategy and what that means for DevOps professionals. The video of the conversation is below, followed by a transcript.[embedded content]
Announcer: This is Digital Anarchist.
Alan Shimel: Hey, everyone, welcome to another Tech Strong TV segment. My guest right now is Philip Rathle. Philip is with Neo4j, VP Product. Hey, Philip, welcome to Tech Strong TV.
Philip Rathle: Thanks, Alan. Pleasure to be here.
Alan Shimel: Absolutely. Philip, we’ve interviewed folks from Neo4j before, but I don’t know if everyone in our audience is familiar with the company. So, why don’t we start there? Why don’t you give us a little company background, maybe a little bit of your own background as well?
Philip Rathle: Sure. Let me start with the second and tie it in. The thing that really attracted me to this company when I joined a bit over nine years ago was the idea that the most valuable thing in data, in my opinion, has always been the model and what’s built into the model. It’s how things are related, and what this company was doing was leaning into that some more at a time where the data space—and you look at everything that happened in NoSQL was about really simplifying the data model so that you could do this very, very high level volume and scale, but very simple data problems, store and retrieve. And in order to do that, you can lighten up on certain of the more technically complex requirements like, you don’t need acid if you’re not doing complex relationships and store and retrieve of shopping cart data at a massive scale.
And so, what Neo4j’s been doing for more than a decade now is solving the problem of how to understand context and causality in your data through looking at how things are related. So, if you look at my relationship with—I don’t know, let’s look at an insurance scenario—with my car, other people who drive my car, other vehicles I might own, other things that I might own like a house and then line on that, and a business and lines on that and then bringing that together, suddenly, you’re into these complex kinds of enterprise data scenarios that have always been a challenge, persistently. And I lived through a lot of those as a consultant trying to fix those, kind of on a one-off basis.
And so, what Neo4j did was something that, in the database space at the time was a bit crazy is build an engine ground up that assumed that you could do random I/O really well. Because if I’m going to understand relationships, a query can go any which way through the data, potentially very, very deep if I’m in a 20-level deep supply chain, for instance, following an item or a big payment network and looking for fraud detection, the fraud patterns or money launderings so that I can catch those. Those are all about patterns in the data, so Neo4j is a company that’s built a database from the ground up, looking to help users understand the relationships and the data so that they could understand context, causality, second order, third order, fourth order effects more deeply.
Alan Shimel: Got it, got it. What about a little of your own background?
Philip Rathle: I did consulting for the first half or third of my career, and a lot of that culminated in ending up as the person between the business and the IT problem. I really liked understanding what problems were being solved and then using new technology and seeing what we could bring to bear to solve the business problems.
And I ended up working a lot with data and databases and a lot of large systems. I started at Accenture back when it was Anderson Consulting, then went into kind of a boutique company that worked on high end, large database deployments called Tanning Technology. And there, I realized I was doing—I later realized this was a step towards product management or I guess today you would call it product management. And ultimately, co-founded a startup, tried that for a few years around master data management, customer data integration; wrong market, wrong time—that’s maybe a story for another day. And then got into Embarcadero Technologies, which makes database tools.
Alan Shimel: Mm-hmm.
Philip Rathle: So, I worked there for about seven years, ended up heading up the database product portfolio, about a dozen tools for managing databases as well as a database interface database. Then learned about Neo4j, so, all of the cool stuff that was happening in data about 10 years ago, and thought, “I wanna be a part of this, kind of ground up, creating new ways of managing data.” And yeah, the rest is history. I’ve been heading up Product Management since then. I was employee number 30, and we’re at 550 growing way past 600 this year.
Alan Shimel: Excellent. Very cool. So, I think we’ve laid a lot of the ground work, here. Phil, recently Neo4j announced a rather large raise. Why don’t you give us—a capital raise—why don’t you give us some information on that?
Philip Rathle: Yeah, we did. So, as a company, you know, those of you who, probably every listener knows how it works with startups, right? You raise money, you put that money into primarily R&D, improving the product, and other areas of the business, and then you grow and, you know, at some point, there’s some kind of exit for us. We’re looking to be a big, independent company, and for that, databases are highly capital intensive, very complex technologies. It’s not enough to have just the database, you need tooling around it, especially when it’s a new kind of model.
So, we just made a massive fundraise. We had already raised over $100,000,000.00 over the course of the years. This single fundraising event is $325,000,000.00, it’s the biggest fundraise ever for any database company in history. So, we’re pretty excited about it.
Alan Shimel: For good reason—congratulations. You know, I’ve been involved in companies that have raised money, significant money over the years—that’s a lot of money, man. [Laughter] That’s a really big raise.
Now, of course, you know, our audience says, “Whew, another big number.” We’re hearing a lot of big numbers lately. What does it mean for them? What does it mean for them, what does it mean for Neo4j to have that kinda capital? What’s the payoff, here?
Philip Rathle: Yeah, I mean, it’s pretty much product, product, product with a little bit of a dash of regional expansion. So, on the product side, Neo4j recently launched—so, late 2019, we launched a self-serve version of the database as a service. So, it starts at $65.00 a month, credit card swipe. You can get a fully managed Neo4j database in the cloud and turn it on, it runs. You don’t need to worry about managing the database. And we launched an enterprise tier last year. Right now, it runs on, you can choose between GCP, AWS, of course, Azure and others are coming, and Work in Progress.
So, one area is to really keep improving the cloud product since the preferred way of consuming databases these days has flipped to cloud very, very fast over—you know, whereas just in the last couple years. Another one is graph data science. So, last—a bit over a year ago, we entered the, we expanded what Neo4j can do, which typically has been a developer oriented database with database drivers and more for transactional operational applications.
What we’ve been seeing over the last few years is that a lot of the kind of next step in machine learning and data science and AI is getting information about the connections in the data and in the network into your machine learning algorithm. And that whole art and science is graph data science, and we have a graph data science for a Neo4j offering, which is a graph data science library, together with a database, together with Bloom, which is a visualization and exploration tool. And that offering has really taken off in the last year, both in terms of demand, customers, and also, it’s become really mature in a short time. But there’s still a long way to go; it’s a brand-new space.
So, those are the two primary areas, and then I’ll add the third is just continuing to mature the database, continuing to make it scale, handling larger and larger amounts of data. By the way, as part of our announcement at our annual conference, we also a trillion-relationship graph.
Alan Shimel: Wow.
Philip Rathle: Just showing how, across 1,000 machines like—actually, trying to spin up this many machines on AWS, Amazon couldn’t provision the machines fast enough. [Laughter] We’ve never seen this kind of error.
Alan Shimel: Right.
Philip Rathle: But there’s no end to performance, scalability, security, more features, more richness, et cetera. So, those are the main areas, and like I said, a dash of regional expansion, really growing beyond—our primary markets have been U.S. and Europe with some customers in other parts of the world, but this will help us expand more in other parts of the world.
Alan Shimel: Excellent. You know, I think one lesson that I learned during my career, and I think it’d be wise to share with the audience is—you know, there’s a latency time between the time you raise money and the time that money finds its way into products that are released, right?
Philip Rathle: Yeah, that’s very true.
Alan Shimel: Because, okay, now we’ve raised money, we gotta go hire people. We have the idea of what we wanna do, right? But, you know, we gotta flesh out those plans, we gotta get the people power, the technology process in place.
When do you think we’ll see some of these great things that you’re talking about that, you know, this money raise has enabled actually come out to market?
Philip Rathle: That’s a great question. So, prior to the fundraise, we were close to maxed out in hiring. And when I say close to maxed out, there’s a certain rate at which you can efficiently hire, and then beyond that, you kind of need to have people on their own off in the background, but that can’t contribute to the code base, because it’s not even structured to be able to have more than a certain number of people contribute. So, we’re—and that’s less true with new areas that we’re growing out. So, we’ve seen this one coming now for a few months, and so, we—that, plus just early in COVID, like, last April, we actually froze hiring for a bit and said we don’t know where things are going and we don’t wanna be in a position where we have to lay people off, that would just be horrible. Let’s just pause hiring.
And then our business kept, you know, fortunately, doing well. And so, we started to wrap up, but I’d say—so, two answers, maybe. One answer is, you’re already starting to see the results of a really aggressive ramp in product and engineering. My team is doubling this year, and it’s close to doubled already. The engineering team is growing by about 50 percent. But what this also means is, we can keep hiring and we can keep—and whenever we have new products, new areas, we can form entirely new teams there.
So, I’d say the answer is now, but it also means that, on a steady state, continued basis, you’re gonna continue to see some really significant product releases from quarter to quarter, year to year.
Alan Shimel: I would imagine. I would imagine—and beyond, with raising that kinda money, beyond. Anyway, Philip, we’re running out of time. You know what, did we mention the website?
Philip Rathle: The website is Neo4j.com.
Alan Shimel: That’s N-E-O—
Philip Rathle: The number 4 and the letter J.
Alan Shimel: Letter J—dot com.
Philip Rathle: And you’ve got all kinds of info, resources for getting started. We’re huge on open source, we’re huge on education. And I should mention that our vocation has been not just making our own company successful—first of all, it’s about making users successful and making an impact in the world. But then not just making an impact in the world by making our product succeed, but making the category succeed. Because this is something that our founder penciled out on a napkin once upon a time.
So, we see ourselves in some ways as helping to grow the graph database space and just, you know, we celebrate when other companies go launch graph databases, because that means it’s good for the world. We’ve done a lot of work in the standards area and so on to make it more mature and better. So, you’ll see an open source version, there’s a free sandbox you can get started with, there’s a free version of our cloud product. Or, both on the developer side, on the data science side, lots of docs, good community out there, a Discord group, questions, QA on ________, you name it, it’s all out there. We’d welcome any and all of you listeners to check it out.
Alan Shimel: Cool. Alright, we’re out. Philip Rathle, Neo4j—thanks for joining us on Tech Strong TV here today. Congratulations to you and the whole team on an outstanding raise, and now, let’s see you put it to work.
Philip Rathle: Thank you.
Alan Shimel: Alrighty. Be well. This is Alan. We’re gonna take a break here on Tech Strong TV. We’ll be right back with our next guest.
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