George Anadiotis, Linked Data Orchestration/ZDNet: “2018 was the Year of the Graph, the year graph databases went mainstream. I have no reason to think this will change, it will only accelerate.

“Before, we’d use a graph to store the data with the machine learning happening in Python. We’re connecting the dots.” Image: Neo4j ...

But Neptune also exemplifies another important development in graph databases: integration of data science and machine learning features.

That’s a learning curve.” Sahni feels that some applications should use a combination of relational and graph databases, with each type powering the individual functions that it does best.

Microsoft's two new video series target beginner developers interested in using Python for machine-learning programs.

While approaches and capabilities differ, all of these databases allow you to build machine learning models right where your data resides.

According to a report by industry observer DB-Engines, “Graph DBMSs are gaining in popularity faster than any other database category,” growing 300 percent since January of last year.