Graph databases work best when the data you’re working with is highly connected and should be represented by how it links or refers to other data, typically by way of many-to-many relationships.

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

Graph database query languages are growing, along with graph databases. They let developers ask complex questions and find relationships.

Fluree touts itself as the Web3 Data Platform -- a semantic graph database that guarantees data integrity, facilitates secure data sharing, and powers connected data insights, all in one pluggable ...

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.

AnzoGraph DB: Driving AI and Machine Insights with Knowledge Graphs in a Connected World Knowledge Graphs are quickly being adopted because they have the advantages of linking and analyzing vast ...

Neo4j has announced the general availability of the next generation of its namesake graph database for both community and enterprise users. Neo4j 5 widens the performance lead of native graphs over ...

Knowledge graphs are on the rise at enterprises that seek more effective ways to connect the dots between the data world and the business world. Paired with complementary AI technologies such as ...

Startups like TigerGraph, MongoDB, Cambridge Semantics, DataStax, and others compete with Neo4j in a graph database market expected to be worth $2.4 billion by 2023, in addition to incumbents like ...