Relational databases have been a fixture of software applications for decades. They are highly tuned for performance and typically offer explicit guarantees like transactional consistency. More recently, there’s been a figurative cambrian explosion of other-than-relational databases. Simple key value stores or counters were an early win in this space. Managing a graph data structure is a more challenging task than key value stores. Asking questions of graphs demands a robust query language. Ideally, you’d also want to provide common graph algorithms to users out of box.
MemGraph is an in-memory graph database that seeks to fill this need in a real-time fashion. It’s a performance driven solution that offers developers a mature option for managing and utilizing graph data. In this interview, I speak with Dominik Tomicevic, Founder and CEO of MemGraph. We discuss implementation and adoption details of MemGraph as well as the ways machine learning engineers use it for feature engineering in real time.
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