Things can connect people, yet it is impossible to map their relationship in advance. Consequently, database administrators have to add each item as a different field and create a relationship between them later – a time-consuming affair and a never-ending task. Graph databases can help database administrators by simplifying the task.
In graph database applications, any given data point can be connected to any other data point at any time as a business user without any need for administrators to rewrite the schema. Graph databases are designed to be scalable and fit well with big data applications. They are quick and allow users to move along the chain of connectors and gain insights quickly and efficiently. Unlike other relational database models, graph databases enable the addition of relationships instantly. Hence, there are many use cases for graph databases.
One of the use cases for graph databases is anomalies and fraud detection. A traditional approach to fraud detection depends on checklists, and consequently, there is a high possibility of missing subtle fraud attempts. Graph databases are designed to spot unusual connections between transactions. Hence, they help identify fraudulent behavior instantly. Furthermore, enterprises defending themselves from hackers need to look for clusters of connected events. With graph databases, they would be able to define the schema, notes, and relationships, without having to define them upfront. This would simplify the identification of the clusters of connected events.
Sometimes unusual connections can be advantageous. Advanced recommendation engines can suggest books, movies, music, fashion, and other production services based on consumer transactions. With the help of graph database applications, recommendation engines can look beyond direct and simple connections to find complicated relations, thereby helping to make their recommendations more diverse and relevant.
Today, many privacy regulations demand that organizations compile all the personal data they have collected about their customers and delete it on request. Since they tend to store all this information in different data sets, graph databases can help connect the data and help organizations comply with data privacy regulations.
In the future, graph databases may include advanced machine learning and artificial intelligence applications, thereby revolutionizing the way these technologies are handled. In addition, relational databases and graph databases together can help to build business systems of the future.
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