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It's also very important to mention that this whole graph space also became much more mature in terms of how people look at graph as they have developed and learned more about the technology. We now have more discussions about what is the additional value our product provides. We now have fewer discussions with developers about whether ArangoDB has the right data model. In the beginning we struggled a little bit with the graph database market, where users tried to determine if graph was the right fit. When you look at AI, for example, there is hardly ever an initial data set where everything is structured, so you need to be able to handle different types of data. It's a full-feature graph database with all the stuff you also get from any other graph database and it can handle different data models. We have branded our multimodel technology as graph and beyond. Weinberger : Graph has always been the main component of our database, but we also have capabilities to handle all sorts of other data models. Where does graph technology fit into the database landscape today?
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What is clear to us now is that having a scalable graph database that can handle structured and unstructured data at the same time is really a huge benefit for many people. I've heard that it can take an average of seven years for a new database to gain traction and have a lot of users really start to use it in production. In the space of graph analytics and graph AI, there really isn't an alternative to using a graph database. Users make the decision whether they should use a graph database or stick with a relational database as there are advantages to each type of database. What has changed from my perspective is there are now more graph analytics and AI use cases. Claudius Weinberger : It has taken some time for the graph database market to develop in a nice way and you can see that now with the funding that Neo4j got.