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

Imagine your database of choice blown out of the water by a startup emerging from stealth. TigerGraph may have done just that for graph databases.

Aerospike Inc., maker of a highly scalable NoSQL database, today is entering the graph database market with an offering that it claims can outperform and outscale offerings from market leaders ...

The rise of graph databases is closely related to AI's demand for data processing. AI technology requires vast amounts of structured and unstructured data, which must not only be input into ...

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 ...

Graph databases have been around in one form or another since the early oughts, but they were generally slower, more complex to work with, and more limited in terms of their applicability than ...

The addition of vectors provides context to the graph database for enhanced search and supports generative AI and large language models.

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.

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

TigerGraph Inc. has launched its managed graph database on Google Cloud, enabling customers of the search giant’s infrastructure-as-a-service platform to use the software in their analytics ...