Data-hungry AI applications are fed complex information, and that's where graph databases and knowledge graphs play a crucial role.

The addition of vectors provides context to the graph database for enhanced search and supports generative AI and large language models.

There are other native graph databases, which means the data is stored in a graph-like manner and not added on after the fact, as with some graph solutions that reside atop Hadoop and NoSQL. Neo4j is ...

Victor Lee is director of product management at TigerGraph. Graph databases excel at answering complex questions about relationships in large data sets. But they hit a wall—in terms of both ...

Graph databases excel for apps that explore many-to-many relationships, such as recommendation systems. Let’s look at an example Jeff Carpenter is a technical evangelist at DataStax. There has ...

For example, when mapping the best driving route between two points, a graph database can efficiently process and analyze the various routes, road conditions, and traffic data.

TigerGraph’s eBook “Native Parallel Graphs: The Next Generation of Graph Database for Real-Time Deep Link Analytics,” discusses what developers need to learn in order to leverage the power of graph ...