Graph databases are really less about the volume of data or availability and more about how your data is related and what calculations you’re attempting to perform. As Philip Rathle, senior director of product engineering at Neo Technologies (makers of Neo4j), told me, graph databases are especially useful when “the data set is fundamentally interconnected and non-tabular. The primary data access pattern is transactional, i.e., OLTP/system of record vs. batch… bearing in mind that graph databases allow relatedness operations to occur transactionally that, in an RDBMS world, would need to take place in batch.” Common uses for graph databases include geospatial problems, recommendation engines, network/cloud analysis, and bioinformatics — basically, anywhere that the relationship between the data is just as important as the data itself. This is also an important technology in various financial analysis functions. If you want to find out how vulnerable a company is to a bit of “bad news” for another company, the directness of the relationship can be a critical calculation. Querying this in several SQL statements takes a lot of code and won’t be fast, but a graph database excels at this task.Read the whole article here.