![]() ![]() We open sourced it when we were at the company. Michael Schwartz: Was there a community at that time for Neo4j?Įmil Efreim: No, we were always like a single-vendor, vendor-lead open source, so we created the software internally. We started a first round of seed funding in 2009 at the back of NoSQL happening. NoSQL started happening, and that’s when we said all right, let’s spin out this into a separate company. At that point, big data was starting to get a hold. So then, fast forward a bunch of years towards the end of the 2000s, so 2007, 2008, 2009. We disagreed with that perspective but felt like it was impossible to change the entire discourse in this industry. The industry was just coming off this big hangover, if you will, from the object databases in the mid-to-late ‘90s, which kind of flamed out very quickly.Īt that point, everyone’s kind of talking – yeah, the Ruby innovations in data, it’s just going to build on top of the relational database. But when we looked at the industry, at the time there was really no discussions around alternative databases. ![]() We were working at an Enterprise content management company at the time, and we had a lot of data that was very connected, so we solved it for our own use. When we first solved the problem, or at least started solving the problem, it was, like you mentioned, early 2000, and that was just a fully internal use. Michael Schwartz: You and your co-founders identified the need for a graph database around 2000, and you spent a couple of years trying to tackle the super hard problem.Īt what point did you decide it was the right time to start the company behind this database?Įmil Efreim: That’s a good question. And if you have that kind of data, we can typically query it and be a thousand times faster, even a million times faster than a traditional relational database. It turns out that a lot of data, in the modern day and age, is very connected. It’s a way of modeling data that is highly connected. When I say graph, you should think social graph, which is a synonym with network. So, through that, you can build up a graph. But unlike the databases that most people are familiar with relational databases, which shape that data in rows and columns – our building blocks, or abstractions, are nodes and then relationships between those nodes. One that I’ve spent most of my career actually answering and broadcasting.Īt the highest level, we’re a database, so we store data. But just to provide some context, what is a graph database and what is it good at?Įmil Eifrem: That is a great question. ![]() Michael Schwartz: So, primarily this is a business podcast. What is a Graph Database?Įmil Eifrem: Thanks, Mike. I think I only scratched the surface, so enough of my blabbering, here it goes. Not surprisingly, Emil has learned a few things along the way, so for this reason, it was a lot of fun chatting with him. The list goes on, and pretty much every challenge in the book was faced by Neo4j. It’s hard to build a business around open source. It’s hard to build an open source community from scratch. It’s hard to raise capital if you are based in Sweden. It’s hard to build a new business model in your totally new market segment. It’s hard to invent a totally new market segment. This week, we’re lucky to have with us Emil Eifrem, one of the co-founders and the CEO of Neo4j.Įverything about Neo4j’s journey was difficult. Michael Schwartz: Welcome back, Underdogs. In this episode, Emil identifies key questions entrepreneurs must ask in the emerging era of public cloud software. Emil Eifrem is the Co-founder and CEO of Neo4j, a category-defining graph database platform powering applications for artificial intelligence, fraud detection, real-time recommendations, and master data. ![]()
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