Introduction to network telemetry using Apache Kafka in Confluent Cloud

The use of telemetry is an increased focus in IT operations providing raw data to the Machine Learning / Artificial Intelligence (ML/AI) algorithms for AIOps (Artificial Intelligence for IT Operations).

Network operators have relied upon SNMP and Syslog to monitor the network. Network telemetry (streaming data pushed to a collector) is replacing the polling of network devices. The push approach is less burden to the CPU of the device, can be delivered promptly, and is initiated by the device when a state change is detected.

There are open source tools to receive telemetry data, store it, visualize and alert; how should the network operator provide access to infrastructure telemetry data, in real-time, at scale across all technology stakeholders?

This session, presented by Joel King, illustrates publishing telemetry data from the Meraki SDK to Apache Kafka deployed in Confluent Cloud. Kafka is a distributed event store and stream-processing platform designed for big data and high throughput. Using the developer instance of Confluent Cloud and the Python SDK, we examine the ease at which a network operator can publish and consume telemetry data to implement its own AIOps approach.