Graph-Analytics

Graph Databases: Beyond SQL Graph databases store data as nodes and the relationships between them. This makes connections an explicit part of the model, not something added later with joins. When data is highly interconnected—social networks, supply chains, or fraud rings—a graph view often feels natural and fast. SQL databases can do these patterns with multiple joins, but the cost grows with data size and path length. Graph databases optimize traversals: stepping from one node to its neighbors is built into the engine, so deep or complex traversals stay responsive. ...

Graph Databases and Their Use Cases Graph databases store data as nodes and edges. They focus on relationships. In a property graph model, each node and edge can hold properties like names, dates, or weights. This design makes traversing connections fast and predictable, even as data grows. When data is tightly connected, graphs help you find patterns quickly. A social network, for example, can map people as nodes and friendships as edges. Queries that follow paths, not just single lookups, become simple and fast. ...