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 ...

Over the past decade, we’ve seen a wave of diversification followed by consolidation in database technologies. Rela­tional databases such as Oracle, MySQL, and SQL Server com­pletely dominated ...

Graph databases offer a more efficient way to model relationships and networks than relational (SQL) databases or other kinds of NoSQL databases (document, wide column, and so on). Lately many ...

It's often easier to understand the use cases for graph databases than understanding how graph databases work. For instance, asking the question of who the most powerful thought leaders across ...

IBM Corporation (US), Oracle (US), Microsoft Corporation (US), AWS (US), Neo4j (US), RelationalAI (US), Progress Software (US), TigerGraph (US), Stardog (US ...

Graph databases, which explicitly express the connections between nodes, are more efficient at the analysis of networks (computer, human, geographic, or otherwise) than relational databases. That ...

A new generation of graph databases has taken hold, and a generation of query languages has arrived alongside them. The assorted graph database query languages include the likes of Gremlin, Cypher, ...

Understanding the relationships in graph database theory allows us to work with the new 'shape' of data itself. Businesspeople like graphs. C-suite executives are fond of pie charts, Venn diagrams, ...

AI thrives on data but feeding it the right data is harder than it seems. As enterprises scale their AI initiatives, they face the challenge of managing diverse data pipelines, ensuring proximity to ...

Oracle 1.0 was more about databases than device graphs. But as Oracle methodically built out a Marketing and Data Cloud, that changed. For the latter, marrying data management platform BlueKai with ...