Scenario: A company is experiencing data processing bottlenecks while integrating Hive with Apache Kafka due to high message throughput. How would you optimize the integration architecture to handle this issue efficiently?

Optimizing the integration architecture involves techniques such as partitioning Kafka topics, configuring consumer groups, implementing data compaction, and scaling resources. These measures ensure efficient handling of high message throughput and alleviate data processing bottlenecks. By addressing these aspects, organizations can enhance the performance and scalability of Hive with Apache Kafka integration, enabling smoother data processing for analytics and other applications.