Apache Kafka® is a crucial component in modern digital infrastructures, facilitating real-time data processing across various applications such as financial transactions and IoT data streams due to its high throughput and low latency capabilities. However, any disruption to Kafka can have severe repercussions, emphasizing the need for robust high availability (HA) and disaster recovery (DR) strategies. Ensuring Kafka's resilience involves understanding its fault-tolerance features, simulating catastrophic scenarios, and maintaining monitoring and maintenance protocols. Critical aspects include managing network partitions, leader elections, and ensuring time synchronization across components. Testing Kafka's resilience through chaos engineering and regularly updating DR playbooks are essential practices. Furthermore, maintaining DR readiness as Kafka clusters scale involves scaling DR components, monitoring replication lag, and automating infrastructure management. Finally, the document highlights the importance of disabling unclean leader elections, focusing on empirical validation through testing, and provisioning DR clusters to handle peak loads to ensure robust performance and recovery capabilities.