From my experience, I have found that a properly designed schema in an RDBMS is going to outperform a graph database for OLTP style queries _if_ your graph trav

From my experience, I have found that a properly designed schema in an RDBMS is going to outperform a graph database for OLTP style queries _if_ your graph traversal is light. So, if you just need to build a system that finds similarities and you only need to look at a level or two of relationships, its going to be more simple to use an RDBMS, possibly. But, if you need to do a lot of graph traversing, this is where and RDBMS system is going to get tricky. One caveat with graph databases is that there is different kinds with different strengths. Some are fast and in-memory, some are distributed and meant more for OLAP. Some are distributed with reads, and are meant for OLTP but are slow for writes. While there is similar tradeoffs with different RDBMS systems, if your use case is to get something up and running, I would start with an RDBMS and keep it simple until you can't. Finally, your final paragraph may be one path forward, start with an RDBMS, and if you can't leverage to do what you need quickly but its working for most of your use case, you can extract the relationships to a graph database and use it alongside.