Handling high data loads in Kafka: Protecting hardware resources and ensuring system resilience

The exponential growth of data streams has made platforms like Apache Kafka indispensable for real-time data processing. However, managing high data loads while protecting hardware resources remains a challenge, especially in cloud environments. This paper presents a dynamic, cloud-native Kafka architecture that integrates rate-limiting, dynamic scaling, and data prioritization to prevent resource exhaustion and optimize system performance. This approach demonstrates significant improvements in message latency, lag clearance, and cost efficiency, offering a practical solution for modern data-intensive applications.