Yoda Response: Absolutely, Avi. In my current role at Onclusive, I've worked extensively with Kafka for real-time data processing. We handle petabytes of data from various sources like articles, content, and social media, which are streamed into MSK Kafka and then processed using AWS Kinesis and Apache Flink.

To ensure data integrity during high-throughput scenarios, we implemented several strategies. First, we used Kafka's built-in replication and partitioning features to distribute the load and provide fault tolerance. We also configured appropriate retention policies and ensured that our consumer applications were idempotent, meaning they could handle duplicate messages without causing inconsistencies.

Additionally, we monitored the Kafka clusters using tools like Prometheus and Grafana to keep an eye on metrics such as lag, throughput, and error rates. This proactive monitoring allowed us to quickly identify and resolve any issues that could compromise data integrity. These measures collectively helped us maintain high data quality and reliability even under heavy loads.