Denormalization techniques for IOT data warehouses: balancing query performance and data redundancy

Authors

  • M.V. Talakh Yuriy Fedkovich Chernivtsi National University, Chernivtsi
  • V.V. Dvorzhak Yuriy Fedkovych Chernivtsi National University
  • Yu.O. Ushenko Yuriy Fedkovych Chernivtsi National University

DOI:

https://doi.org/10.31649/1681-7893-2025-49-1-72-81

Keywords:

IoT data warehouse, denormalization techniques, query optimization, columnar storage, data compression, smart home analytics, Azure Synapse, schema design, performance optimization, data redundancy

Abstract

 This article explores the impact of denormalization techniques on query performance in IoT data warehouses while maintaining acceptable data redundancy. It analyzes normalized and denormalized approaches in a smart home IoT environment using Azure Synapse. Empirical testing (10,000–5 million records) shows that strategic denormalization combined with columnar storage optimization improves performance by up to 94%. Evaluating four key optimization techniques (Join Reduction, Columnar Storage, Query Complexity Optimization, Temporal Scaling Optimization), we find that denormalization initially increases storage needs by 16% (120 GB vs. 103.5 GB), but columnar compression reduces the final storage size by 50.4% (17.1 GB vs. 34.5 GB). The study provides practical insights into balancing query performance and data redundancy in high-speed IoT environments.

Author Biographies

M.V. Talakh, Yuriy Fedkovich Chernivtsi National University, Chernivtsi

Ph.D., Assistant professor of Computer Science Department, Yuriy Fedkovich Chernivtsi National University, Chernivtsi

V.V. Dvorzhak, Yuriy Fedkovych Chernivtsi National University

Ph.D., Assistant professor of Computer Science Department

Yu.O. Ushenko, Yuriy Fedkovych Chernivtsi National University

D.Sc.,Professor, Head of Computer Science Department

References

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Published

2025-06-18

How to Cite

[1]
M. Talakh, V. Dvorzhak, and Y. Ushenko, “Denormalization techniques for IOT data warehouses: balancing query performance and data redundancy”, Опт-ел. інф-енерг. техн., vol. 49, no. 1, pp. 72–81, Jun. 2025.

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Section

OptoElectronic/Digital Methods and Systems for Image/Signal Processing

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