Google popularized the term "knowledge graph" in this 2012 blog post. Since then, there has been a massive momentum around ...

Getting Your Data Graph-Ready After deciding on the technology and the key operational and logistical questions you want to answer with graph, your next step is to build a graph data model.

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.

As data complexity continues to grow and the demand for real-time insights increases, the move away from traditional relational databases and towards the adoption of graph databases will become vital.

First, we’ll define and demystify these terms. Second, I’ll share some key business use cases that cannot be solved with traditional relational data catalogs. Finally, I’ll wrap it up by getting ...

A: Well, graphs are particularly good at allowing the user to think about, discover and learn about connections between data items. That's the whole point; these connections are first-class citizens.

A graph representation of coauthorship, taken from either data set, might look like a triangle, showing that each mathematician (three nodes) had collaborated with the other two (three links).

The semantic technologies underpinning RDF knowledge graphs are primed for data mesh and data fabric architectures — and their synthesis. They’re certainly ideal for crafting data products.