Choosing the Right Data Store for Network Device Utilization Levels

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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.

A graph database approach relies on persisting relationships in the storage layer instead, which leads to highly efficient graph retrieval operations. Azure Cosmos DB's Gremlin

API supports the property graph model.

For a database that illustrates the relationships between utilization levels of individual network devices across a local area network, the most suitable data store would be a graph database.

A graph database is designed to represent complex relationships between data, such as the relationship between network devices and their utilization levels in this scenario. Graph databases use nodes to represent entities and edges to represent the relationships between them. Each node can have properties that describe it, while edges can have properties that describe the relationship between nodes.

In this case, each device on the network would be represented by a node, and the utilization levels would be represented by properties of the node. The edges between the nodes would represent the connections between the devices. This would allow for efficient querying and analysis of the utilization levels of individual devices, as well as their relationships to other devices on the network.

Key/value stores are typically used for simple data retrieval, and document databases are designed to store and retrieve unstructured data, while columnar databases are designed to efficiently store and query large amounts of data. None of these data stores are well-suited for representing complex relationships between data, making them less appropriate choices for this scenario.