Graphs and data modeling have become essential components of data analytics. One common way to model data is through a graph database. In this blog, we’ll explain in basic terms what a graph database is and how you can use them to get insightful analytics.
What is a Graph Database?
A graph database is commonly depicted as a network of nodes. Each node is meant to represent an entity, like a person or a place. These nodes are connected and each connection represents a relationship between nodes. Each relationship contains a quantitative piece of data that shows how or why the nodes are connected. For instance, a very simple example of this would be the two nodes, Paul and Dan. The relationship “is the son of” might connect the two. So in the graph you would see these two separate nodes, Paul and Dan, and through the connection you could see that Dan is the son of Paul.
Once a database is created, it’s simple for a user to enter a query and find the relationship between nodes.
These network of nodes are unique in the fact that they’re specifically centered around relationships and connections, rather than data about specific individuals.
What Can Graph Databases Do?
So we have a little bit better of an understanding of what a graph database is and what kind of data it has, but how do we use this data? There are plenty of use cases.
It turns out that they're used in more places than you might think. They’re commonly utilized by major companies for their recommendation engines, to improve their artificial intelligence, to detect fraud, and much more.
For instance, you can utilize graph databases to improve your AI chat messenger. They will help the AI learn what common queries are and make those connections to help your customers get the right answer. That’s just the beginning of what these databases can offer. As the technology improves and they become more widely utilized, they will continue to change and shape the way business is done and how we collect data.
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