ArangoDB advances graph database fortunes with new funding

ArangoDB said on Oct. 6 it raised $27.8 million in a Series B round of funding, bringing the total raised for the vendor to $47 million.

Founded in 2014, ArangoDB has built out a graph database technology that also supports multimodel database capabilities, enabling it to handle both structured and unstructured data.

The database vendor is based in San Francisco, with European headquarters in Cologne, Germany. It has grown its technology over the years, launching its ArangoDB Oasis database-as-a-service cloud platform in 2019.

The market for graph database technology has been active in 2021, as demand for the technology has continued to increase and vendors have attracted strong financial backing. Among the other graph database vendors that have raised money this year is Neo4j, which raised $325 million on June 17.

In this Q&A, Claudius Weinberger, co-founder and CEO of ArangoDB, discussing the challenges and opportunities for graph databases.

Why are you raising money now?

Claudius Weinberger

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.

What has changed from my perspective is there are now more graph analytics and AI use cases. 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. In the space of graph analytics and graph AI, there really isn't an alternative to using a graph database.

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. 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.

Where does graph technology fit into the database landscape today?

Weinberger: Graph has always been the main component of our database, but we also have capabilities to handle all sorts of other data models.

We have branded our multimodel technology as graph and beyond. 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. 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.

In the beginning we struggled a little bit with the graph database market, where users tried to determine if graph was the right fit. We now have fewer discussions with developers about whether ArangoDB has the right data model. We now have more discussions about what is the additional value our product provides.

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.

Graph technologies are the foundation for modern data and data analytics. I think it's really now become obvious to many people that graph adds value, because handling relationships is key to getting value from data.

What multimodel capabilities beyond graph are most commonly used today?

The use case has changed from users doing analytics on a simple social network graph to using a graph database to doing complex analytic stuff to get more insight from a big data set.

Weinberger: We started initially with JSON [JavaScript Object Notation] and that is still the main demand that we are seeing. We have also added in schema support and we see a lot of people using that capability. With schema support, it's possible to develop applications faster.

Schema is also useful to help maintain data quality and provide performance improvements.

What are the key challenges you have seen with graph database adoption?

Weinberger: The challenge we have seen is users doing increasingly complex analytics on a graph at large scale. The use case has changed from users doing analytics on a simple social network graph to using a graph database to doing complex analytic stuff to get more insight from a big data set.

Making it easier to run the database is another challenge. That's why we started our own managed service. What is also important is to have this service running on all the main cloud providers so it's easy for users to switch and users don't have to worry about being locked into one cloud provider's environment.

Editor's note: This interview has been edited for clarity and conciseness.