Relationships

Graph Databases and Connected Data Graph databases store data as nodes and relationships, with properties on both. This mirrors how we see the world: people linked by friendships, products tied to categories, and events connected to places. With this structure, traversing paths of multiple steps becomes natural, not a heavy join in a distant table. They shine when queries focus on connections. You can ask for patterns, short paths, or communities, such as who is connected to a partner company through colleagues, or which customers form a dense network around a product. In fraud detection or recommendations, the value of knowing “who knows whom” or “which item is linked to similar buyers” is clear. ...

Graph Databases for Connected Data In many apps, data comes with many links. Users connect to friends, products relate to categories, and devices talk to services. A graph database stores not only items but also the links between them. This makes it easier to answer questions like who is connected to whom, or which paths lead to a goal. Compared with traditional databases, graph stores focus on relationships. Data is modeled as nodes (entities) and edges (relationships). You can add properties to both nodes and edges to describe details like a person’s age or the strength of a connection. With this setup, traversing a network becomes fast, even when the data grows large. ...