A NoSQL data store that captures the relationships among records to create a logical network of associations that business users can query. It is ideal for tasks such as documenting the interaction of friends on a social media platform.
When relationships are the important aspect to the data, graph databases shine. The data does not have to be “big” for the graph database to provide significant performance benefits over other database technologies. The data itself can be homogenous, such as all people and their relationships as in a “social graph” or heterogenous.
Graph databases are poised to expand dramatically in the next few years as the nature of what is important and worth saving in an enterprise has expanded dramatically beyond alphanumeric data and into relationships. Network databases, the distant predecessor to graph databases, lost their luster when number crunching became the key part of most workloads but in a highly connected world where power has reverted to the individual in control of their relationships where “group think” is involved in those relationships, it is imperative to understand them. All the nodes in a network could be people in order to understand their relationships or it could be a variety of objects that have relationships that need to be navigated quickly such as name, address and order.
Most knowledge graphs reside in a graph database. Although theoretically, one could build a knowledge graph on any type of data store, given the fact that it’s a graph, it’s best to use a store specifically designed to handle graph data. Graph databases differ from traditional relational databases because they treat relationships between objects as first-class citizens. There are two main types of graph databases: RDF Triple Stores and Labelled Property Graphs