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

Min Yin

doi:10.70393/6a69656173.333833

OPEN ACCESS |Research Article ||4 February 2026

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

Min Yin

University of California-Berkeley

gmiayinc@gmail.com

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

* Corresponding Author¹: Min Yin, E-Mail: gmiayinc@gmail.com

Publication

Accepted 2026 January 25 ; Published 2026 February 4

Journal of Industrial Engineering and Applied Science, 2026, 4(1), 3005-6071.

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.

Keywords

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

Metadata

DOI:

10.70393/6a69656173.333833

ARK:

ark:/40704/JIEAS.v4n1a07

Pages: 49-61

References: 27

Disciplines: Computer Science

Subjects: Semiconductor Analytics

Cite This Article

APA Style

Yin, M. (2026). Optimizing cloud-native lakehouse architectures for real-time semiconductor analytics: balancing performance, cost, and energy efficiency. Journal of Industrial Engineering and Applied Science, 4(1), 49-61. https://doi.org/10.70393/6a69656173.333833

Acknowledgments

Not Applicable.

FUNDING

Not Applicable.

INSTITUTIONAL REVIEW BOARD STATEMENT

Not Applicable.

DATA AVAILABILITY STATEMENT

Not Applicable.

INFORMED CONSENT STATEMENT

Not Applicable.

CONFLICT OF INTEREST

Not Applicable.

AUTHOR CONTRIBUTIONS

Not application.

References

1.

Chen, Y. (2025). Artificial Intelligence in Economic Applications: Stock Trading, Market Analysis, and Risk Management. Journal of Economic Theory and Business Management, 2(5), 7-14.

2.

Pahune, S., & Akhtar, Z. (2025). Transitioning from MLOps to LLMOps: Navigating the unique challenges of large language models. Information, 16(2), 87.

3.

Sun, Y., & Ortiz, J. (2024). An AI-Based System Utilizing IoT-Enabled Ambient Sensors and LLMs for Complex Activity Tracking. Academic Journal of Science and Technology, 11(3), 277–281.

4.

Chen, Y. (2025). Daily Asset Pricing Based on Deep Learning: Integrating No-Arbitrage Constraints and Market Dynamics. Journal of Computer Technology and Applied Mathematics, 2(6), 1-10.

5.

Lin, A. (2025). Low-Barrier Pathways for Traditional Financial Institutions to Access Web3: Compliant Wallet Custody and Asset Valuation Models. Frontiers in Management Science, 4(6), 80-86.

6.

Qi, Z. (2025). Root Cause Tracing Algorithm and One-Click Repair Mechanism for Medical Server Failures. Journal of Progress in Engineering and Physical Science, 4(5), 43-48.

7.

Qi, Z. (2025). Design of a Medical IT Automated Auditing System Based on Multiple Compliance Standards. Innovation in Science and Technology, 4(9), 17-23.

8.

Bhimji, W., Carder, D., Dart, E., Duarte, J., Fisk, I., Gardner, R., ... & Würthwein, F. (2023). Snowmass 2021 computational frontier compf4 topical group report storage and processing resource access. Computing and Software for Big Science, 7(1), 5.

9.

Chen, Y., & Xu, J. (2026). Deep Learning for US Bond Yield Forecasting: An Enhanced LSTM–LagLasso Framework. ICCK Transactions on Emerging Topics in Artificial Intelligence, 3(2), 61-75.

10.

Yin, M., & Frank, L. F. (2026). Multi-Modal Fusion for Yield Optimization: Integrating Wafer Maps, Metrology, and Process Logs with Graph Models. ICCK Transactions on Emerging Topics in Artificial Intelligence, 3(1), 45-60.

11.

Qi, Z. (2025). Design and Practice of Elastic Scaling Mechanism for Medical Cloud-Edge Collaborative Architecture. Journal of Innovations in Medical Research, 4(5), 13-18.

12.

Luo, M., Du, B., Zhang, W., Song, T., Li, K., Zhu, H., ... & Wen, H. (2023). Fleet rebalancing for expanding shared e-Mobility systems: A multi-agent deep reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems, 24(4), 3868-3881.

13.

Yin, M. (2025). Drift-Aware Streaming Predictive Maintenance for Semiconductor Equipment.

14.

Petrovic, A. J. (2025). Design of Secure and Fault-Tolerant Patterns for AI-Driven Healthcare Analytics in Hybrid Cloud Platforms. International Journal of Research and Applied Innovations, 8(6), 13027-13033.

15.

Gujjala, P. K. R. (2023). The Future of Cloud-Native Lakehouses: Leveraging Serverless and Multi-Cloud Strategies for Data Flexibility. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 868-882.

16.

Chen, Y. (2025). Leveraging LSTM Networks for Vehicle Stability Prediction: A Comparative Analysis with Traditional Models under Dynamic Load Conditions. Computing and Interdisciplinary Science, 1(2), 15-22.

17.

Li, K., Chen, X., Song, T., Zhou, C., Liu, Z., Zhang, Z., Guo, J., & Shan, Q. (2025a, March 24). Solving situation puzzles with large language model and external reformulation.

18.

Wang, H. (2022). Supervised Learning for Complex Data (Doctoral dissertation, The University of North Carolina at Chapel Hill).

19.

Aires, V. A. J. (2025). Optimising Energy Analytics: a Dimensional Modelling Approach for Enhanced Decision-Making (Master's thesis, Universidade NOVA de Lisboa (Portugal)).

20.

Chen, Y. (2025). A Comparative Study of Machine Learning Models for Credit Card Fraud Detection. Academic Journal of Natural Science, 2(4), 11-18.

21.

Chen, T. (2025). Innovative Approaches in Art Vocational Education: Exploring Industry-Academia Collaboration and Internationalization. Journal of Advanced Research in Education, 4(5), 11-17.

22.

Wang, H., Li, Q., & Liu, Y. (2024). Multi-response Regression for Block-missing Multi-modal Data without Imputation. Statistica Sinica, 34(2), 527.

23.

Kretzer, A. R., Benitti, F. B., & Siqueira, F. (2025). Challenges and Opportunities in Big Data Analytics for Industry 4.0: A Systematic Evaluation of Current Architectures. IEEE Access.

24.

Nyunt, A. T., Kotak, B., Chauhan, R., Jain, R., Parmar, K. J., Palaniappan, D., & Premavathi, T. (2026). Next Generation Data Warehousing for Destination Marketing With Big Data Technologies. In Maximizing Destination Marketing Strategies in the Digital Era (pp. 157-194). IGI Global Scientific Publishing.

25.

Zhang, K. (2025). Research on the Dynamic Control Path of Procurement Costs Driven by Information Technology Tools: A Case Study of the QG Project. Innovation in Science and Technology, 4(8), 10-15.

26.

Wang, H., Li, Q., & Liu, Y. (2023). Adaptive supervised learning on data streams in reproducing kernel Hilbert spaces with data sparsity constraint. Stat, 12(1), e514.

27.

Nuthalapati, A. (2025). Scaling AI Applications on the Cloud toward Optimized Cloud-Native Architectures, Model Efficiency, and Workload Distribution. International Journal of Latest Technology in Engineering, Management & Applied Science, 14(2), 200-206.

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