So this started to look like a case for a graph database - these are the types of queries graph databases excel at. Apart from being ugly to write and maintain, those queries don't perform well either.
Machine learning features may be derived from other features, which means ending up with a lot of joins, and especially a lot of self joins. Relational systems are not a good match, Schad said. So they wanted to have a common layer with all the metadata where this would end up being one query. Just being able to identify the different ML models deployed in production was very challenging because they had to go through a number of different metadata stores - for the ML part, the data feature transformation part, and so on. With a PhD in database systems, distributed data analytics, and large scale infrastructure container systems, Schad has been switching between databases. Schad joined ArangoDB last year but has been working with ArangoDB for the past four years. Today ArangoDB is a US company with a German subsidiary, it has a new chief revenue officer, Matt Ekstrom, and a new head of engineering, Schad. That means that we found a way that we can combine the JSON document data model, the graph model, and the key-value model in one database core with one query language." "The main idea for ArangoDB, what is still valid today, is what we call the native multi-model approach. As Weinberger noted, he and his co-founder have been working together for 20 years, and the decision to pursue their vision was not a spur of the moment idea:
The team made the headlines in 2019 with their $10 million in Series A funding led by Bow Capital. ArangoDB was founded in Cologne in 2014 by OnVista veterans Claudius Weinberger and Frank Celler.