Systems | Development | Analytics | API | Testing

What’s remarkable is how long Confluent competed at the highest level. Creating a category and type of application is hard; transitioning to cloud and surviving against hyper scalers is even harder. That alone is a huge achievement. Some see this as a pressured exit. But another way to look at it is as a strategic purchase by IBM to strengthen its position in enterprise data movement and integration.

When we talk about JSON schema in the world of Kafka and streaming, you may assume the schema for the events/messages. But how many times have you fumbled in the configuration about trying to get an application deployed? Schemas that describe how to configure and deploy Applications or applications-as-code, are also important, allowing us to automate application landscapes. Especially as we will soon be a wash with catalogs for AI Agents, MCP servers etc.

Picture this: It's 3 AM. You’re on-duty in case there is an outage. A team in the other part of the world merged PR and released a new version of K2K Replicator and it crashed. Consumer group lag is spiking to the universe. You’re paged & woken up, went to your laptop, the team already reverted PR, things are stabilising, but what really happened, you have to investigate now as postmortem has to be done.

If you’ve been running Kafka for a while, with any luck you have quite a few engineering teams onboarded, potentially with hundreds or even thousands of applications. Hopefully the Lenses.io Developer Experience platform helped in this adoption. But finding the right balance between governance and openness can be tricky.