Graph Databases 101: Your Top 5 Questions with Non-Technical Answers

The modern business environment is highly connected and dynamic, with networks such as social media, recommendation engines, and supply chains playing critical roles in the success of enterprises. However, legacy databases struggle to keep up with these changes due to their rigid structure and infrequent modifications. Graph databases, on the other hand, are built to store and retrieve connections from the ground up, making them more flexible, scalable, and agile than relational databases. They are particularly well-suited for applications that leverage artificial intelligence and machine learning, as AI and ML thrive on connected data. Graph databases store two kinds of data: entities (vertices) and the relationships between them (edges). Native graph databases like TigerGraph use index-free adjacency to physically point between connected vertices in the database, ensuring highly performant connected data queries. The use cases for graph technology are vast and diverse, with applications ranging from revenue generation and risk management to improving operational efficiency and foundational technology.