Graph databases facilitate discovery and analysis closely connected facts. This post is one of a series that introduces the fundamentals of NOSQL databases, and their role in Big Data Analytics.

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

A startup named TigerGraph emerged from stealth today with a new native parallel graph database that its founder thinks can shake up the analytics market.

A new semantic-based graph data model has emerged within the enterprise. This data model has all of the advantages of the relational data model, but goes even further in providing for more ...

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.

Graph databases will never replace conventional relational databases, but for harnessing the value of the fundamental interconnectedness of everything, a graph database is well worth considering.

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

Neo4j is both the original graph database and the continued leader in the graph database market. Designed to store entities and relationships, and optimized to perform graph operations such as ...

Although databases that focus on the relational aspect of data analytics abound, few are as effective at revealing the hidden valuable insights as a graph database.

Graph databases that use nodes and properties to represent and store data appear to be making significant inroads in the U.S. retail market. For example, graph database specialist Neo Technology of ...