How do you use hybrid cloud for real-time data processing applications?

Hybrid cloud integrates private and public cloud resources to provide flexibility, cost optimization, and security for real-time data processing applications, which is crucial for dynamically handling streaming data scenarios such as financial transactions and IoT monitoring. Its core includes private cloud for processing sensitive data and public cloud for elastic scaling, with seamless data transmission through API connections and real-time streaming engines (such as Apache Kafka), which in practice enhances response time and supports large-scale analysis. Implementation steps: 1) Design data pipeline segmentation (private processing, public scaling); 2) Deploy stream processing tools (such as Flink) and integrate cloud services (such as AWS Kinesis); 3) Monitor and optimize performance. Typical scenarios include real-time inventory analysis, and the business value lies in rapid decision-making, risk reduction, and improved resource utilization.