Apache Kafka, an event streaming platform, does not inherently support message prioritization, a common feature in traditional messaging systems like JMS and AMQP, due to its architectural design focused on immutable data streams and efficient data processing. Kafka's architecture revolves around a commit log, ensuring data immutability and ordered record processing, which inherently conflicts with the concept of message reordering based on priority. To address the lack of prioritization, a "bucket priority pattern" is proposed, where messages are grouped into different priority buckets determined by custom partitioners and assignors, allowing consumers to process messages according to priority without altering Kafka's core principles. This pattern involves producers and consumers determining bucket allocations and processing messages accordingly, though it introduces complexity in managing partitions and requires careful configuration. Despite these challenges, the bucket priority pattern offers a practical solution for implementing message prioritization within Kafka's event streaming framework, maintaining the platform's strengths in scalability, fault tolerance, and parallelism.