Architecture design and performance optimization of real-time processing platform for massive data streams

This study focuses on the architecture design and performance optimization of the real-time processing platform for massive data streams, aiming at solving the challenge of efficiently processing high-speed, massive and changeable data streams in the field of information technology. The platform adopts a distributed and modular architecture, covering four core levels: data source and acquisition, data processing engine, data storage and management, and data visualization and monitoring. In the data processing engine layer, Apache Flink is selected as the core framework, and the real-time and batch processing capabilities are integrated with Lambda architecture to realize the immediacy and comprehensiveness of data processing. The data storage and management layer adopts Apache HBase to support high concurrent access and large-scale data storage. Performance optimization strategies include parallelization and partitioning, data compression and serialization, caching mechanism, and resource optimization and dynamic allocation to improve processing efficiency and resource utilization. In addition, this study also discusses the optimization of fault tolerance and disaster tolerance to ensure the high reliability and stability of the system. By applying the platform to the case analysis of Intelligent Transportation Systems (ITS), the effectiveness of the platform in improving the accuracy of traffic monitoring, optimizing public transport services and supporting traffic planning is verified.