Relational databases are perhaps the most commonly used data management systems. In relational databases, data is modeled as a collection of disparate tables. In order to unify the data within these tables, a join operation is used. This operation is expensive as the amount of data grows. For information retrieval operations that do not make use of extensive joins, relational databases are an excellent tool. However, when an excessive amount of joins are required, the relational database model breaks down. In contrast, graph databases maintain one single data structure---a graph. A graph contains a set of vertices (i.e. nodes, dots) and a set of edges (i.e. links, lines). These elements make direct reference to one another, and as such, there is no notion of a join operation. The direct references between graph elements make the joining of data explicit within the structure of the graph. The benefit of this model is that traversing (i.e. moving between the elements of a graph in an intelligent, direct manner) is very efficient and yields a style of problem-solving called the graph traversal pattern. This session will discuss graph databases, the graph traversal programming pattern, and their use in solving real-world problems.