Key-value, document-oriented, column family, graph, relational… Today we seem to have as many kinds of databases as there are kinds of data. While this may make choosing a database harder, it ...

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 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.

Neo4j is the most popular graph database on the market these days. While graph databases are part of the NoSQL movement, they really solve different problems than, say, Couchbase or MongoDB.

The appetite for connected data is fueling a shift from traditional relational databases to interconnected graph-based models. This evolution promises deeper insights and can facilitate a more ...

Graph database experts hope that better benchmarks could play a similar role in the evolution of graph databases. Ronald sees a need for more graph database benchmarks in verticals.

The flexibility offered by a knowledge-graph-powered data catalog enables near-immediate support for new types of data sources; a knowledge graph makes it easy to extend the model to represent ...

For a graph database like Neo4,j that is nothing. We have the links with the nodes in different versions of hierarchies. I think that we have in the dimension of tens of thousands of nodes.

Graph database company Neo4j wants to move beyond providing only its graph database, and is working on what it calls a 'graph platform' to help companies make the most of their data.

With today’s updates to its managed graph database for the cloud, Neo4j AuraDB, the San Mateo, California-based company is working to make it easier for users to get started with its graph database.