Graph databases work best when the data you’re working with is highly connected and should be represented by how it links or refers to other data, typically by way of many-to-many relationships.

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.

Knowledge Graphs are quickly being adopted because they have the advantages of linking and analyzing vast amounts of interconnected data. The promise of graph technology has been there for a decade.

Amazon’s Neptune is a distributed graph database that’s optimized for very large datasets and fast response times. It works with two popular query languages (TinkerPop and SparQL).

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

AWS makes Neptune, its graph database service, serverless Kyle Wiggers 9:13 AM PDT · October 27, 2022 ...

Graph-powered machine learning and analytics are enabling unprecedented benefits, Victor Lee, head of product strategy and developer relations at TigerGraph, who cited Gartner's statement that 'Graph ...