It is also worth noting that many (not all) graph products are schema-free. Semantic query support tends to be particularly strong in triple stores.Īnother major point is that research suggests that graph visualisations are very easy and intuitive for users. For these types of requirements graph databases not only perform way better than relational databases but they allow some types of query that are simply not possible otherwise. They do not cater well for problems (such as bill of materials – a classic case) that are many-to-many and they also perform poorly when queries involve (perhaps multiple) self-joins. In the latter case, relational databases are inherently limited to one-to-one, many-to-one and one-to-many relationships. Common use cases – especially for analytics – is anything to do with networks, whether these be pipelines, communications or criminals.

Graph databases handle a class of issues that are too structured for NoSQL and too diverse for relational technologies. In the latter case, there are a variety of graph algorithms supported by vendors that may be implemented for machine learning purposes. Both concurrent analytics (where the analytics is separate from operational processes, for example in supporting real-time dashboards) and in-process analytics (where the analytics are embedded in real-time operational processing) may be supported. Compared to some other approaches to this, graphs have the major advantage that the data only needs to be stored once. Given that both graph and RDF databases target both transactional environments and have query processing capabilities, these are an obvious candidate for supporting hybrid processing whereby the database is used for both transactional/operational processing and real-time analytics. In this last category there is also a distinction between vendors targeting known-known problems as opposed to those that also cover known-unknowns and those tackling unknown-unknowns: the most intractable of all. All graph products target analytics but different products are targeted at operational analytics (those suitable for transactional environments) or are pure-play analytic databases. Both property graph and RDF databases may be ACID compliant and both are frequently targeted at transactional environments. RDF databases target semantic processing, often with the ability to combine information across structured and unstructured data.