What is an advantage of using model-driven telemetry in the network?

Model-driven telemetry typically uses a push-based model (subscription-based streaming) rather than traditional pull-based polling, which makes it more efficient and provides near real-time data.

While MIBs (Management Information Bases) are well-known, model-driven telemetry primarily uses YANG data models, which offer more robust capabilities for defining configuration and state data than SMI-based MIBs.

Model-driven telemetry leverages standardized data models (e.g., YANG) and efficient encoding formats (like JSON or Google Protobuf) to stream data from network devices. This modern approach provides structured, easily parsable data that is highly compatible with a wide variety of automation, monitoring, and analytics tools and platforms available on the market, facilitating easier integration and analysis compared to traditional methods.

Traditional methods often rely on parsing unstructured CLI output, which is fragile and error-prone; model-driven telemetry avoids this by streaming structured data directly from the device's internal data models.