Kafka is an open source event streaming platform that generates data feeds for applications to share data efficiently and in real time. Its distributed architecture, which involves multiple "brokers" sharing data with multiple applications, ensures that data streams remain available even if part of the cluster fails. Monitoring Kafka metrics is critical to identify issues with applications relying on data streams, but traditional approaches like server-side monitoring can be limited by a lack of granularity and root cause focus. Leveraging tools like eBPF (extended Berkeley Packet Filter) provides a better approach to Kafka performance monitoring, allowing for accurate, actionable visibility into Producer-Consumer latency and enabling integration with broader monitoring strategies.