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

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.

Still using an RDBMS for friend-of-a-friend queries? Big mistake. Enlist a graph database using Neo4j instead.

The appetite for connected data is fueling a shift from traditional relational databases to interconnected graph-based models.

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.

Emerging graph database benchmarks are already helping to overcome performance, scalability and reliability issues.

The flexibility offered by a knowledge-graph-powered data catalog enables near-immediate support for new types of data sources; a knowledge graph makes it easy to extend the model to represent ...

Graph databases are powerful new tools for managing and analyzing heterogeneous data across the enterprise. Most importantly, organizations are beginning tounderstand the specific use cases that graph ...

Real-time database vendor Aerospike is expanding its multi-model capabilities with the launch of the Aerospike Graph database. Aerospike got its start back in 2009, providing a NoSQL database that ...

Automotive giant Daimler is using Neo4j's graph database technology in its HR department. ZDNet spoke to Jochen Linkohr, the manager of HR IT at Daimler, to find out more. ZDNet: When did you ...

With today’s updates to its managed graph database for the cloud, Neo4j AuraDB, the San Mateo, California-based company is working to make it easier for users to get started with its graph database.

New techniques make graph databases a powerful tool for grounding large language models in private data.