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.

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.

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

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 ...

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 ...

The Nebula Graph project was kicked off in May 2019, and most of that year, says Ye, the company focused on creating the foundation for its distributed graph database. At the end of 2019, Ye started ...

A sui generis, multi-model open source database, designed from the ground up to be distributed. ArangoDB keeps up with the times and uses graph, and machine learning, as the entry points for its ...

First, we’ll define and demystify these terms. Second, I’ll share some key business use cases that cannot be solved with traditional relational data catalogs. Finally, I’ll wrap it up by getting ...