By multi-model, Franz said its semantic graph database supports ingestion of different JSON documents as well as Resource Description Framework (RDF), or triplestore, data—another World Wide Web ...

This is where graph databases and NoSQL come into play. Unlike relational databases, which work particularly well with structured data, graph databases are designed to model and store data as ...

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 ...

The second step in generating a knowledge graph involves building a text prompt for LLM to generate a schema and database for the ontology. The text prompt is a natural language description of the ...

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.

Graph is a data model that has long lingered on the fringe of mainstream adoption. But that is changing, as graph lends itself well to representing many real world problems, and the technology is ...

As the name suggests, native graph databases are those specifically built to handle graph workloads across the entire computing stack. The opposite, non-native, databases come in two flavors: those ...

The wrong database, or poor implementation, can utterly undermine your work. So get to know what’s what with NoSQL databases to suit your own Big Data analytics needs.

Some graph database products on the market are really wrappers built on top of a more generic NoSQL data store. This virtual graph strategy has a double penalty when it comes to performance.

Neo4j, which offers a graph-centric database and related products, announced today that it raised $325 million at a more than $2 billion valuation in a Series F deal led by Eurazeo, with ...