They are used extensively for computing graph algorithms such as shortest path and PageRank. When you need to efficiently perform calculations on the connections between data, graph databases are a great option. Graph databases can enable organizations to easily perform impact analysis and plan for outages.Ĭomputing graph algorithms. Because these entities are physically connected in the real world, they map naturally to graphs. Graph databases can help IT specialists model and manage networks, IT infrastructure, and IoT devices. Graph databases allow an application to analyze previous purchase data to determine what the customer may want to purchase next.

Modern businesses want to provide intelligent recommendations to their customers as a strategic way to increase revenue. Graph databases allow analysts to look at the connections between the data to find patterns of fraud.īuilding recommendation engines. Rather than working from a single point or account, they create a fraud ring of smaller schemes that are much more likely to go undetected. In recent years, criminals have become increasingly sophisticated in their schemes. Graph databases are commonly used for the detection of money laundering, credit card fraud, insurance fraud, tax fraud, and other criminal activity. Some of the most common are detecting fraud, building recommendation engines, managing IT networks, and computing graph algorithms between data.ĭetecting fraud. Graph databases are suited for a variety of use cases. The diagram below shows a graph with several nodes, edges, properties, and labels.Ī graph that represents airports, airport restaurants, and flights. You might choose to apply a label like 'Airport' to the airport nodes and 'Restaurant' to the restaurant nodes. In addition to the nodes for airports, you might create nodes for other related entities such as airport restaurants. An edge represents a flight from Auckland Airport to Brisbane Airport, with properties for airplane and airline. The edge could have properties like airplane: 73H 388 and airline: Qantas.Ī node represents Auckland Airport, with its properties listed. In this example, you could create an edge between the nodes for Auckland Airport and Brisbane Airport. The edge's properties contain information about the flight. For example, you could create a node for Auckland Airport in New Zealand with properties such as airport_code: AKL and country: New-Zealand.Ī flight between two airports-that is, the relationship between two nodes-is represented by an edge. Let's say you want to store air travel information.Įach airport would be represented by a node, and information about the airports would be stored in the nodes' properties.

Let's walk through how data is stored in a graph database.

How is Data Stored in a Graph Database? An Example The section below contains an example of how data is modeled in a graph database using nodes, edges, properties, and labels. A label can optionally be used to tag a group of related nodes. A property is a key-value pair that stores information about a particular node or edge. You can think of edges as the verbs in your databases they store information about the actions that are taken between the nodes. You can think of nodes as the nouns in your databases they store information about people, places, and things. Graph databases use the following key terms: This data model allows graph databases to prioritize relationships between the data, which makes searching for connections and patterns in the data easier than it is with traditional databases. A graph database is a database that stores data in nodes and edges-nodes for information about an entity and edges for information about the relationship or actions between nodes.

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