Out of the plethora of misconceptions common in the industry[1], quite a few are squeezed into this paragraph:
“The relational databases that emerged in the 80s are efficient at storing and analyzing tabular data but their underlying data model makes it difficult to connect data scattered across multiple tables. The graph databases weve seen emerge in the recent years are designed for this purpose. Their data model is particularly well-suited to store and to organize data where connections are as important as individual data points. Connections are stored and indexed as first-class citizens, making it an interesting model for investigations in which you need to connect the dots. In this post, we review three common fraud schemes and see how a graph approach can help investigators defeat them.”
--AnalyticBridge.DataScienceCentral.com
Relational databases did not emerge in the 80s (SQL DBMSs did);
- There is no "tabular data" (the relational data structure is the relation, which can be visualized as a table on a physical medium[2], and SQL tables are not relations);
- Analysis is not a DBMS, but an application function (while database queries, as deductions, are an important aspect of analysis, and computational functions can be added to the data sublanguage (as in SQL), the primary function of a DBMS is data management)[3];
- A data model has nothing to do with storage (storage and access methods are part of physical implementation, which determines efficiency/performance[4]).
Here, however, we will focus on the current revival (rather than emergence) of graph DBMSs claimed superior -- without any evidence or qualifications -- to SQL DBMSs (not relational, which do not exist) that purportedly "make it difficult to connect data scattered across multiple tables". This is a typical example of how lack of foundation knowledge and of familiarity with the history of the field inhibit understanding and progress[5].