Graph databases and relational databases have big differences when it comes to how connections work, among other things ...

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

Whereas relational databases require careful attention to schema design in order to optimize performance, graph databases handle new data elements with relative ease, said Comcast’s Hashimoto.

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 ...

If the history of relational databases is any indication, what is going on in graph databases right now may be history in the making.

Graph databases are inherently more flexible than traditional relational database systems because it is possible to treat the metadata about the database as data itself, accessible in exactly the ...

Sahni feels that some applications should use a combination of relational and graph databases, with each type powering the individual functions that it does best.

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

Graph-relational database developer EdgeDB Inc. is gearing up for prime time after closing on a $15 million early-stage round of funding ahead of its official launch early next year.