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Use cases involving social networks, recommendation engines, fraud detection, network analysis, and anyone needing to analyze relationships between entities.
Data is stored as nodes (entities) and edges (relationships) with properties describing each. Queries traverse these connections quickly to find patterns or paths that traditional relational databases struggle with.
Graph DBs focus on relationships and connections, while SQL DBs organize data in tables without native link handling.
They require learning graph concepts and query languages like Cypher but are intuitive once basics are clear.
Yes, many offer distributed setups to handle big volumes of connected data.
Social network analysis, where relationships between people are key, is a classic example.
No special hardware needed, but performance depends on infrastructure and dataset size.
No products available
