For a few decades, structured data are typically arranged as relational tables and stored in relational databases. Recent years have witnessed the blossom of graph databases, for which graph becomes ...

Graph NoSQL database Neo4j version 5 supports query language and performance improvements, automated scale-up and scale-out capabilities, and cross-platform deployment interoperability. Neo4j team ...

At the recent Cloud Next conference in Tokyo, Google announced Spanner Graph, a managed feature that integrates graph, relational, search, and AI capabilities within Spanner. This new database ...

GraphQL was never conceived as a query language for databases. Yet, it's increasingly being used for this purpose. Here's why, and how.

Graph database query languages are growing, along with graph databases. They let developers ask complex questions and find relationships.

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

Graph databases are making a splash in the database market, with specialist, multimodal and cloud database suppliers jostling for a slice of the pie.

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.

TigerGraph, provider of an ML and AI graph analytics platform, is releasing TigerGraph graph database v 3.9.3, introducing extended support for workload management, Kubernetes, and OpenCypher.