Optimizing Cloud-Native Lakehouse Architectures for Real-Time Semiconductor Analytics: Balancing Performance, Cost, and Energy Efficiency

Currently, semiconductor data analysis requires processing massive amounts of real-time data, and traditional data warehouses face challenges in meeting the demands for low latency and high-concurrency queries. Therefore, this paper proposes a cloud-native Lakehouse architecture specifically designed for real-time semiconductor analysis. By introducing an innovative query routing mechanism and data lineage tracing framework, a dynamic multi-tiered storage system is designed. This system can tier data based on access frequency to achieve efficient storage and faster query performance. This research provides a practical solution to overcome the limitations of existing architectures and offers valuable insights for the future development of cloud-native platforms for real-time industrial analysis.