The year of the graph is here. Do you really need a graph database, and, if yes, how do you choose one?

It’s official: graph databases are a thing. That’s the consensus here on Big on Data among fellow contributors Andrew Brust and Tony Baer. When AWS enters a domain, it officially signals the upward slope of the hype cycle. It’s a bit like newfound land – first it’s largely unknown and inhabited by natives, then the pioneers show there are opportunities, then the heavyweights will try to colonize it.

The recent unveiling of AWS Neptune seems to have convinced even once self-proclaimed graph skeptics such as Brust and Baer. Why now, you ask? Much like Machine Learning for example, it’s not so much that there is a major breakthrough in the technology, rather it’s mostly a matter of maturation.

Hardware and software capabilities such as cheap storage and processing capacity in the cloud and on premise, understanding of the challenges in techniques for distributed indexing and querying of graphs and the realization of having big and connected enough datasets have all contributed to the perfect graph storm.

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