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.

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 ...

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.

CrowdStrike’s groundbreaking graph technologies, which started with the company’s renowned Threat Graph, form a powerful, seamless and distributed data fabric, interconnected into a single ...

With the existing capabilities of Neoj4, Eifrem said that the graph database captures explicit relationships between concepts. What vectors do is draw out implicit relationships in data.

Graph analytics is a set of analytic techniques that shows how entities such as people, places and things are related to each other. Unlike traditional data analytics, which is slow and unable to ...

The data referenced in the graph is not based on direct temperature measurements either, he said. Instead, temperatures are inferred based on the types of oxygen locked in the ice core samples.