With data relationships at their center, graph databases are highly efficient when it comes to query performance, even for deep and complex queries.

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 ...

Key-value, document-oriented, column family, graph, relational… Today we seem to have as many kinds of databases as there are kinds of data. While this may make choosing a database harder, it ...

Graph databases provide a way to organize by classes, not tables, are web-aware, and are superior... [+] for managing metadata than traditional relational or NoSQL data stores.

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.

Non-native graph databases are adapted to support graph-like functionality on top of other database systems like relational or NoSQL, providing flexibility and using the strengths of the ...

Transactional cloud databases come in all shapes and sizes, from simple key-value stores to planet-scale distributed relational databases. Here’s how to choose the right cloud database for your ...

Graph databases are finding new use cases in sales, ecommerce, healthcare, financial services, fraud detection and much more.

Graph databases are part of a group of technologies of non-relational databases commonly grouped under the ‘NoSQL’ name and movement.

Companies that want to use powerful graph algorithms to explore hidden connections in their data may want to check out TigerGraph, which today unveiled a pair of cloud-based offerings designed to ...