Graph data science is when you want to answer questions, not just with your data, but with the connections between your data points — that’s the 30-second explanation, according to Alicia Frame.

Choosing a graph database for a particular application depends on several factors, including the complexity of data relationships, scalability requirements, performance needs, and functionalities ...

Names like “network,” “tree,” “taxonomy,” “ancestry” or “hierarchy” suggest that graph technologies would be more effective at analyzing your data than row/column approaches.

Just getting data in and out of property graph solutions is an exercise in patience and improvisation -- good luck representing a graph structure in CSV, and mapping that from solution to solution.