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.

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.

We had a chance to speak with TigerGraph's incoming head of product R&D, and it spurred some thoughts on where we thought graph databases should go.

By combining ontology and large language model-driven techniques, engineers can build a knowledge graph that is easily queried and updatable.

The goal of this type of database is to make it easier to discover and explore the relationships in a property graph with index-free adjacency using nodes, edges, and properties.

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 database vendor Neo4j announced today new capabilities for vector search within its graph database. Neo4j’s namesake database technology enables organizations to create a knowledge graph ...

This can make it quite slow as opposed to a graph database that is densely connected and easily queried. As sensors become more widely used in wearables such as Google Glass, the demand for graph ...

Transactional cloud databases come in all shapes and sizes, from simple key-value stores to planet-scale distributed relational databases. Here’s how to choose the right cloud database for your ...