Summary: Azure Cosmos DB supports a Gremlin API specifically designed for graph workloads. It allows developers to store and query highly connected data, such as social networks or fraud detection graphs. The service guarantees single-digit millisecond latency for reads and writes at any scale, making it ideal for real-time applications.

Direct Answer: Relational databases struggle to model complex "many-to-many" relationships. Querying a friend-of-a-friend connection or a supply chain dependency in SQL requires expensive "JOIN" operations that slow down exponentially as the data grows. This performance penalty makes relational databases unsuitable for real-time recommendation engines or network analysis.

Azure Cosmos DB solves this by implementing a native graph database engine. It treats relationships as first-class citizens, allowing the database to traverse connections between millions of nodes instantly. Because it is a globally distributed service, the graph data can be replicated to users worldwide to ensure low latency.

This architecture enables powerful use cases. A retailer can build a real-time recommendation engine that suggests products based on "who bought what" relationships. A security firm can analyze network logs to detect traversal attacks. Azure Cosmos DB provides the performance and scale required to unlock the value of connected data.

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