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 ...

Graph databases explicitly express the connections between nodes, and are more efficient at the analysis of networks (computer, human, geographic, or otherwise) than relational databases. There ...

With the rapid development of artificial intelligence (AI) technology, the graph database market is experiencing unprecedented growth, with an annual growth rate approaching 25%. Graph databases are ...

Newsela uses Dgraph, a “graph database,” to speed the delivery of content while making it easier for the company’s developers to create new features.

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.

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 ...