Jeff Carpenter is a technical evangelist at DataStax.
There has been a lot of hype recently about graph databases. While graph databases such as DataStax Enterprise Graph (based on Titan DB), Neo4, and IBM Graph have been around for several years, recent announcements of managed cloud services like AWS Neptune and Microsoft’s addition of graph capability to Azure Cosmos DB indicate that graph databases have entered the mainstream. With all of this interest, how do you determine whether a graph database is right for your application?What is a graph database?
Before we go any further, let’s define some terminology. What is a graph database? Think of it in terms of the data model.

A graph data model consists of vertices that represent the entities in a domain, and edges that represent the relationships between these entities.

Because both vertices and edges can have additional name-value pairs called properties, this data model is formally known as a property graph.
Some graph databases require you to define a schema for your graph—i.e. defining labels or names for your vertices, edges, and properties prior to populating any data—while other databases allow you to operate without a fixed schema.To read this article in full, please click here

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