Collecting internal, operational telemetry from Confluent Cloud services and thousands of clusters is no small feat. Traditionally, this data needs to be collected in multiple ways to satisfy all the different requirements. However, this sometimes leads to discrepancies between various systems. With OpenTelemetry, we can collect data in a vendor-agnostic way. Many vendors already integrate with OpenTelemetry, which gives us the flexibility to try out different observability solutions with minimal effort, without the need to rewrite applications or deploy new agents.

Focused on optimizing Kafka performance with maximized efficiency, Confluent’s Product Infrastructure team has been actively exploring opportunities for scaling out Kafka clusters. They are able to run Kafka workloads with half the typical memory usage while saving infrastructure costs, which they have tested and now safely rolled out across Confluent Cloud. In this episode, Adithya Chandra explains how.

Running operations on the cloud in a scaling organization can be time consuming, error prone, and tedious. This episode addresses manual upgrades and rolling restarts of Confluent Cloud clusters during releases, fixes, experiments, and the like, and more importantly, the progress that’s been made to switch from manual operations to an almost fully automated process. Rashmi Prabhu, a software engineer on the Control Plane team at Confluent, has the opportunity to help govern the data plane that comprises all these clusters and enables API-driven operations on these clusters.

Learn about the five different parts to a business value framework: (1) baseline, (2) target state, (3) quantified benefits, (4) unquantified benefits, and (5) proof points; cost effectiveness with Confluent Cloud; how to measure ROI vs. TCO; and a retail example from a customer that details their implementation of an event streaming platform.

Coming from decades of experience in messaging, Dan Rosanova discusses the pros and cons of cloud event streaming services on GCP, Azure, and Confluent Cloud. He also compares major stream processing and messaging services: Cloud Pub/Sub vs. Azure vs. Confluent Cloud, and outlines major differences among them.