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.

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

Graph data science is when you want to answer questions, not just with your data, but with the connections between your data points — that’s the 30-second explanation, according to Alicia Frame.

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.

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 databases are powerful new tools for managing and analyzing heterogeneous data across the enterprise. Most importantly, organizations are beginning tounderstand the specific use cases that graph ...

Automotive giant Daimler is using Neo4j's graph database technology in its HR department. ZDNet spoke to Jochen Linkohr, the manager of HR IT at Daimler, to find out more.

TigerGraph today rolled out a new deal that allows customers to store up to 50 GB of data in a distributed graph database running on-premise, matching what it already offered in the cloud. The company ...

Faster data loading to build graphs quickly Faster execution of parallel graph algorithms Real-time capability for streaming updates and inserts using REST Ability to unify real-time analytics ...

Graph databases, based on mathematics known for three centuries, are starting to yield value for businesses beyond Facebook and Twitter.