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.

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

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 ...

Neo4j®, the leading graph database and analytics platform, today unveiled Infinigraph: a new distributed graph architecture now available in Neo4j’s self-manage ...

The new distributed graph architecture promises unified transactional and analytical processing, enabling enterprises to ...

All databases occasionally run into data integrity issues. With graph databases, where data ingestion has historically been the bottleneck, having trust in the data is even more important.

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.

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 ...

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

Event host TigerGraph, which makes a graph database that it claims is the only scalable one available for enterprises, has announced the final agenda and speaker lineup for “ Graph + AI World ...