Scalable Graph Processing on Kubernetes
In recent years Kubernetes has become the default platform for deploying your microservices. A multi-model database such as ArangoDB can help to provide a scalable persistent backend for both graph and document data models for such microservice architectures.
Especially for a combination of unstructured but highly connected data which can be found in a typical recommendation engine, ArangoDB is a great fit as it can natively store and query both data models. Even better, with the new Kubernetes ArangoDB operator one can easily deploy, scale, and operate your ArangoDB cluster on top of Kubernetes.
In this webinar, we discuss how we can use Kubernetes to build an end-to-end recommender system with ArangoDB and Kubernetes and discuss:
- how a multi-model datamodel can be used to build a recommendation engine
- how to deploy, scale, and manage ArangoDB on Kubernetes using the new ArangoDB operator
- An end-to-end demo of a complete recommendation system on Kubernetes
Lars Maier, studied Mathematics at the University of Bonn. Now working at ArangoDB in the cluster team. Currently developing the k8s operator.
Jörg Schad is currently working on ML Infrastructure. In a previous life, he worked on distributed systems at Mesosphere, implemented distributed and in memory databases, and conducted research in the Hadoop and Cloud area. He’s a frequent speaker at meetups, international conferences, and lecture halls.
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