It’s well-known that graph databases represent new categories of analytics capability and potential for machine learning. If you want to create a knowledge graph, understand buyer intent, or create a recommendation engine with PageRank, graph databases and the algorithms they offer simplify this process. In addition, a knowledge graph lends itself well to delivering machine learning insights in both training the algorithms and the deployment of them. We expect these benefits from our graph database systems and they deliver.
The Less-Obvious Reason
However, there is another big benefit that’s often overlooked — one of metadata management and schemas. In certain situations, I’ve noticed that some analytics teams have little time to manage schemas for incoming data. You are given data and asked to produce analytics from it. Handling the schemas and potential changes can be a time-consuming challenge. NoSQL databases have been popular for their ease-of-use due to their flexible schemas. However, graph databases and the power of triples can work to simplify metadata management, too. By configuring all of your data into triples, you limit your need to have to set up rigid schemas, complicated ETL and data transformation, multiple tables, and tricky, expensive JOINs.
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