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

With the rapid development of artificial intelligence (AI) technology, the graph database market is experiencing unprecedented growth, with an annual growth rate approaching 25%. Graph databases are ...

The rise of graph databases is closely related to AI's demand for data processing. AI technology requires vast amounts of structured and unstructured data, which must not only be input into ...

You can think of a graph database as a set of interconnected circles (nodes) and each node represents a person, a product, a place or ‘thing’ that we want to build into our data universe.

Imagine your database of choice blown out of the water by a startup emerging from stealth. TigerGraph may have done just that for graph databases.

The opposite, non-native, databases come in two flavors: those that affix a graph API on top of an existing, native-to-other-kind of database management system, and those that claim multi-model ...

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.

Graph databases power data journalism on the biggest information leak ever.

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 database startup Neo4j raised $320 million at an over $2 billion valuation, highlighting the value of graph databases.