Logz.io's observability platform, which integrates logs, metrics, and traces, faced operational challenges with uneven task distribution within its Kafka Streams application, particularly in its busiest region. This imbalance led to increased latency alerts and operational noise, prompting an investigation that highlighted the uneven assignment of tasks across pods, impacting system stability. By enhancing observability and leveraging metrics, the team identified that Kafka Streams' default configurations, particularly related to task assignment and rebalancing, were not suitable for high-scale operations. They adjusted configurations such as acceptable recovery lag and probing rebalance interval, which successfully stabilized task distribution and reduced latency, thereby significantly improving system performance and reducing the frequency of alerts. The experience underscored the importance of customizing Kafka Streams configurations for specific operational needs and the critical role of advanced observability in diagnosing and resolving complex system issues.