Graph databases use graph structures for semantic queries with nodes, edges and properties to represent and store data. Each node represents an entity and relationships between nodes are defined by how the nodes are associated. This general-purpose structure allows modeling all kinds of scenarios – from a system of roads to a network of devices. If you need to leverage the relationships between your data elements, a graph database might be the strategy for you. Dataconomy brought this information to our attention in their article, “How to Keep Graph Databases Both Flexible and Secure.”
They are becoming very common within industries such as life sciences, healthcare, financial services, government and intelligence. Graphs have proven valuable in these sectors because of the complex nature of the data and need for powerful and flexible data analytics.
In order to leverage data relationships, organizations need a database technology that stores relationship information as a first-class entity. That technology is a graph database.
Melody K. Smith
Sponsored by Data Harmony, a unit of Access Innovations, the world leader in indexing and making content findable.
