Congrats on the funding!
I've been looking into Dgraph (and also played a bit with Badger), as well as other graph databases as a way to store chronographic event data, while enabling rich relationships between the observed artifacts belonging to each event. The problem is that the solutions I find seem an ugly hack compared to a relational solution. Can you point me to any specific Dgraph documentation or case studies for these kinds of workloads?
By chronological event data, you mean timeseries data? Dgraph can be used for storing that, though, it's not specifically designed to store data that "flat".
It should work conceptually. At least, for smaller datasets, it should be alright (gigabytes or something), but for bigger datasets (terabytes), I think a specific TS DB would make more sense.
However, you can take the aggregations from there and store that along with relationships into Dgraph. That'd be a perfect fit.
fyi - We built a product around a way to store chronographic data in a graph database, capable of handling any resource, relationship, property, and/or event detail you can throw at it. It's called IBM Agile Service Manager.
Take a look, feel free to reach out if you'd like to know more:
