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.

A new semantic-based graph data model has emerged within the enterprise. This data model has all of the advantages of the relational data model, but goes even further in providing for more ...

A startup named TigerGraph emerged from stealth today with a new native parallel graph database that its founder thinks can shake up the analytics market.

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 ...

Graph database startup TigerGraph Inc. today announced a major update to its flagship cloud platform with the Savanna release, bringing with it six times faster network deployments and dozens of ...

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 ...

When DataStax acquired Aurelius, a graph database startup last year, it was clear it wanted to add graph database functionality to its DataStax Enterprise product, and today it achieved that goal ...

Graph technology has come a long way, and today the transformative nature of graphs is publicly visible through examples such as financial fraud detection in the Panama and Paradise papers, contextual ...