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.

Graph databases offer a more efficient way to model relationships and networks than relational (SQL) databases or other kinds of NoSQL databases (document, wide column, and so on). Lately many ...

If the history of relational databases is any indication, what is going on in graph databases right now may be history in the making.

“Graph database and analytics is the way forward. Historically, organizations had to rely on data scientists to design their graph analytics solutions.

While still a bit of an outlier, graph-oriented databases continue to find a role in the modern data stack -- thanks largely to AI.

Victor Lee is director of product management at TigerGraph. Graph databases excel at answering complex questions about relationships in large data sets. But they hit a wall—in terms of both ...

At a time when every enterprise looks to leverage generative artificial intelligence, data sites are turning their attention to graph databases and knowledge graphs. The global graph database market ...

Graph databases are the fastest-growing category in all of data management. Here's how to pick one.

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.

Graph analysis is possibly the single most effective competitive differentiator for organizations pursuing data-driven operations and decisions after the design of data capture Neo doesn’t have ...