A sui generis, multi-model open source database, designed from the ground up to be distributed. ArangoDB keeps up with the times and uses graph, and machine learning, as the entry points for its ...

Neo4j also trumpeted the value of graphs as vector databases used in generative artificial intelligence. AI training requires ...

Real-time database vendor Aerospike is expanding its multi-model capabilities with the launch of the Aerospike Graph database. Aerospike got its start back in 2009, providing a NoSQL database that ...

Generally, real-time graph analytics on large datasets can only be provided by a database that uses a graph data model at the most fundamental level. Conse­quently, it seems as if graph databases ...

The world's only multi-model graph database combining relational (PostgreSQL) and graph model Enterprise graph database that integrates legacy data environment Raising $10 million for AgensGraph ...

The new database adds a property graph data model to the existing capabilities of its NoSQL Database and Apache TinkerPop graph compute engine.

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.

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 ...

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 database startup Neo4j raised $320 million at an over $2 billion valuation, highlighting the value of graph databases.

If CIOs want to start exploiting the hidden knowledge and untapped potential in their internal data stores by applying LLMs to them, then building and refining knowledge graphs using proven graph ...