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

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.

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 ...

With graph databases, queries only touch the relevant data. Simpler and More Natural Data Modeling: Anyone who has studied relational database modeling understands the strict rules for satisfying ...

Relational databases are so entrenched and ubiquitous that we reflexively use them for new application requirements. However, graph databases are better for applications with specific processing ...

As data complexity continues to grow and the demand for real-time insights increases, the move away from traditional relational databases and towards the adoption of graph databases will become vital.

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

The relational database is primarily oriented toward the modeling of objects (entities) and relationships. Generally, the relational model works best when there are a relatively small and static ...

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-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.