The world of big data is no longer just taking notice of graph database technology — they’re taking action to implement it because they see its potential. The result is that the industry is now demanding connected data and more of it.
In this week’s 5-Minute Interview (conducted at GraphConnect San Francisco
), Nussbaum discusses how the team at AtomRain uses Neo4j to solve connected data problems across a variety of industries, including L.A.’s home turf: entertainment. We also cover Nussbaum’s thoughts on scalability and the future of graphs in the connected enterprise (B2B) and the connected consumer (B2C).
Talk to me about how you use Neo4j at AtomRain.
Brad Nussbaum: We use Neo4j for the connected enterprise and the connected consumer. We’re trying to make it easy for people to access graph database resources and build scalable systems that are robust, reliable, and stand up to all the enterprise workloads that are out there.
What made you choose Neo4j?
Bradley: We chose Neo4j five years ago because we were facing a lot of challenges with existing database technologies, especially surrounding scaling and connected data. We realized Web 3.0 was moving in the direction of the connected consumer, and that graph technology was going to be an important part of that story.
At the time, Neo4j was the best solution on the market. Today, it’s still blowing everyone else out of the water. It’s a fantastic and reliable graph — being able to trust your graph is, I think, the most important part about developing with Neo4j.
What’s the most interesting result or project you’ve worked on using Neo4j?
Bradley: Our most relatable projects, datenightmovies.com and nextqueue.com, are in the media and entertainment space. We brought together a variety of content sources — such as Netflix, IMDb, Hulu, Vimeo and VUDU — and built a recommendation engine and discovery platform that gives Netflix-style recommendations to customers. These two products are live today.
Knowing what you know today, if you could go back in time and start all over again with Neo4j, is there anything you would do differently?
Bradley: Actually, no, probably because we’ve always been at the bleeding edge of whatever Neo4j is offering. Even now, as soon as 3.1 Beta 1 hit, we integrated it and built in Causal Clustering (the technology formerly known as Core-Edge) solutions. We’ve always worked very closely with all the people on the Neo4j team — engineers, the product roadmap team and project managers — so we’ve always felt very plugged in and connected to the overall organization.
We’ve also had a tremendous amount of support in everything that we’ve built and tried to push out. From collaborative marketing to coordinating product roadmap and release cycles, the access has been fantastic. The relationships are great, and the direction Neo4j has taken over the years to build great management and engineering have just been fantastic.
Is there anything else you’d like to add?
Bradley: I’m really looking forward to the next evolution of Causal Clustering and what it will bring for even more massive-scale graphs. I’d also like to have conversations with Jim Webber and other members of the Neo4j team to see how we can really get to that big scale-out solution that gives us an infinite graph, with thousands of clusters spanning the world. I think that the promise of graph technology is a big vision.
As graphs have grown up, everyone is realizing that there’s so much great potential, and people in the big data space are realizing that data that isn’t connected just isn’t as useful. We need to have connected data, and we need to have more of it, and Neo4j is the right solution for delivering.