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

Min Yin

doi:10.70393/6a69656173.333833

Authors

Min Yin University of California-Berkeley

DOI:

https://doi.org/10.70393/6a69656173.333833

ARK:

https://n2t.net/ark:/40704/JIEAS.v4n1a07

Disciplines:

Computer Science

Subjects:

Semiconductor Analytics

References:

27

Keywords:

Cloud-native Lakehouse, Semiconductor Analytics, Storage Tiering, Columnar Compression, Query Routing, Cost-energy Optimization

Abstract

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.

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Author Biography

Min Yin, University of California-Berkeley

University of California-Berkeley, US, gmiayinc@gmail.com.

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