The appetite for connected data is fueling a shift from traditional relational databases to interconnected graph-based models.

New techniques make graph databases a powerful tool for grounding large language models in private data.

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.

agamemnon is a Python-based graph database built on pycassa, the Python client library for Apache Cassandra. In short, it enables you to use Cassandra as a graph database. The API is inspired by ...

Imagine a graph database that's not aimed at the growing graph database market, selling to Fortune 500 without sales, and claiming to be the fastest without benchmarks. Dgraph is unique in some ...

The Bulgarian graph database startup Graphwise today announced a major upgrade to its flagship GraphDB tool, adding new features aimed at boosting enterprise knowledge management and creating a more ...

"Graph databases offer a data model that allows for the expression of connections in a very robust fashion," Eifrem said. " Relationships in the graph data model are first class citizens. That is not ...

In a graph database, data is represented by nodes, edges and properties. Nodes are the familiar objects that we might model in a RDBMS or key-value store - customers, products, parts, web pages, etc.

Graph visualization in DataStax studio. Photo Credit: DataStax. Graph databases are different from relational in several key ways.

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 ...