Azure Cosmos DB for Apache Gremlin is a graph database service that can be used to store massive graphs with billions of vertices and edges. You can query the graphs with millisecond latency and evolve the graph structure easily. The API for Gremlin is built based on Apache TinkerPop, a graph computing framework that uses the Gremlin query language. Here are some scenarios where graph support of Azure Cosmos DB can be useful: Social networks/Customer 365: By combining data about your customers and their interactions with other people, you can develop personalized experiences, predict customer behavior, or connect people with others with similar interests. Azure Cosmos DB can be used to manage social networks and track customer preferences and data. Recommendation engines: This scenario is commonly used in the retail industry. By combining information about products, users, and user interactions, like purchasing, browsing, or rating an item, you can build customized recommendations. The low latency, elastic scale, and native graph support of Azure Cosmos DB is ideal for these scenarios. Geospatial: Many applications in telecommunications, logistics, and travel planning need to find a location of interest within an area or locate the shortest/optimal route between two locations. Azure Cosmos DB is a natural fit for these problems. Internet of Things: With the network and connections between IoT devices modeled as a graph, you can build a better understanding of the state of your devices and assets. You also can learn how changes in one part of the network can potentially affect another part. Data as it appears in the real world is naturally connected. Traditional data modeling focuses on defining entities separately and computing their relationships at runtime. While this model has its advantages, highly connected data can be challenging to manage under its constraints. Property graph objects A property graph is a structure that's composed of vertices and edges. Both objects can have an arbitrary number of key-value pairs as properties. Vertices/nodes: Vertices denote discrete entities, such as a person, place, or an event. Edges/relationships: Edges denote relationships between vertices. For example, a person might know another person, be involved in an event, or have recently been at a location. Properties: Properties express information (or metadata) about the vertices and edges. There can be any number of properties in either vertices or edges, and they can be used to describe and filter the objects in a query. Example properties include a vertex that has name and age, or an edge, which can have a time stamp and/or a weight. Label - A label is a name or the identifier of a vertex or an edge. Labels can group multiple vertices or edges such that all the vertices/edges in a group have a certain label. For example, a graph can have multiple vertices with a label of "person".