Apache Kafka's Self-Balancing Clusters feature simplifies the process of scaling a cluster by automatically rebalancing partitions and brokers in response to changes in load, traffic, or broker failures. This feature is built into Kafka itself, running on the cluster controller node, continuously collecting metrics about the state of the cluster. When the cluster detects a load imbalance or broker overload, Self-Balancing Clusters computes a reassignment plan to adjust the partition layout and executes the plan while monitoring and throttling to prevent overloading. The feature handles various scenarios such as adding new brokers, decommissioning old ones, handling load changes, and protecting data during rebalancing. It also considers competing priorities like network utilization, disk usage, and replica counts to achieve a balanced cluster. With Self-Balancing Clusters, operators can focus on having their Confluent Platform capacity be exactly what they need, without constantly monitoring and computing adjustments for dynamic changes.