An Introduction to Graph Databases
If you are choosing a platform for a workload, you might consider a graph database if you would describe that workload with any of these words: network, graph, hierarchy, tree, structure, or path.
Graph databases are one of the fastest growing categories in data management, yet they remain an enigma to many. They are part of the NoSQL family because standalone graph databases do not support SQL. However, they are different from other NoSQL databases such as key-value stores, column stores, and document stores; they store a more specific type of data — relationship data — and are less “general purpose.” Graph workloads are among the most decoupled of all workloads.
There are different kinds of graph databases as well. Though all graph databases implement vertices and the edges (relationships) among them, property graphs allow for the addition of “properties” or attributes on the vertices and edges. A graph database can have dozens or billions of vertices and edges. Both extremes can be good uses of graph databases.
The property graph is the style of graph database that has the most market share. The other main style is the RDF (Resource Descriptive Framework) triple store. It is based on a W3C (World Wide Web Consortium) standard so it has many more participants. To create the equivalent of properties on vertices and edges in an RDF-style database, you create a new relationship from the vertex or edge to a vertex with the property.
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