Kafka stores data across partitions in each topic, and each partition has a leader and zero or more followers that fetch and replicate new data from the leader. Unclean leader elections can lead to data loss if not managed properly. Coordinating changes to maximum message size is crucial for smooth messaging pipeline operation. Monitoring system load and other host-level resource metrics can help detect issues with certain brokers processing messages. Investigating data reprocessing issues on low-throughput topics requires adjusting the consumer offset retention period. Kafka's approach to segment-level retention can cause unexpected results if not properly configured, especially in low-throughput topics. The default configuration settings are designed for high-traffic topics, so it's essential to test and adjust settings according to specific use cases.