ArangoDB Advances Graph Database With New Funding

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

Founded in 2014, ArangoDB built a graphical database technology that also supports multimodel database capabilities, allowing it to handle both structured and unstructured data.

the database provider is based in San Francisco, with a European headquarters in Cologne, Germany. It has developed its technology over the years, launching its ArangoDB Oasis Cloud database platform as a service in 2019.

The graphical database technology market has been active in 2021 as the demand for the technology has continued to increase and vendors have attracted strong financial support. Among other graphics database providers who have raised funds this year is Neo4j, which raised $ 325 million on June 17.

In this Q&A, Claude weinberger, co-founder and CEO of ArangoDB, discussing challenges and opportunities for graphical databases.

Why are you raising money now?

Claude weinberger

Claude weinberger: It has taken some time for the graphical database market to develop satisfactorily and you can see it now with the funding obtained by Neo4j.

What has changed from my perspective is that there are now more graphical analyzes and AI use cases. Users decide whether to use a graphical database or stick with a relational database, because each type of database has its advantages. In the space of graphical analysis and graph AI, there really is no 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 many users to really start using 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.

What is the place of graphics technology in the database landscape today?

Weinberger: The chart has always been the main component of our database, but we also have the ability to handle all kinds of other data models.

We have branded our multi-model technology as graphics and beyond. It’s a full chart database with everything you get from any other chart database too and it can handle different data models. When you look at AI, for example, there is hardly ever an initial dataset where everything is structured, so you need to be able to handle different types of data.

In the beginning, we struggled a bit with the chart database market, where users tried to determine if the chart was the right fit. We now have less discussion with developers about whether ArangoDB has the right data model. We now have more discussions about the added value provided by our product.

It’s also very important to mention that all of this graphics space has also become a lot more mature in terms of how people look at the graphic as they develop and learn more about the technology.

Graphics technologies are the basis of modern data and data analysis. I think it really became apparent to a lot of people that the chart adds value because relationship management is the key to getting value from The data.

What multi-model capabilities beyond the graph are most commonly used today?

The use case has shifted from users performing analysis on a simple social network chart to using a chart database to perform complex analysis to get more information from a dataset. voluminous.

Claude weinbergerCo-founder and CTO, ArangoDB

Weinberger: We started out with JSON [JavaScript Object Notation] and this is still the main demand that we see. We have also added in diagram support and we see a lot of people using this ability. With the support for schemas, it is possible to develop applications faster.

The schema is also useful to help maintain data quality and improve performance.

What are the main challenges you have encountered with the adoption of graphical databases?

Weinberger: The challenge we have seen is that users are performing increasingly complex analyzes on a chart at large scale. The use case has shifted from users performing analysis on a simple social network chart to using a chart database to perform complex analysis to get more information from a dataset. voluminous.

Making the database easier to operate is another challenge. That’s why we launched our own managed service. What’s also important is that this service works on all major cloud providers so that it’s easy for users to switch and users don’t have to worry about being locked into the environment of a cloud provider.

Editor’s Note: This interview has been edited for clarity and brevity.

Maria H. Underwood