Relational Database Normalization vs Denormalization: A Performance Perceptive
DOI:
https://doi.org/10.70112/ajes-2025.14.2.4291Keywords:
Relational Database Design, Normalization, Denormalization, Query Performance, SQL ServerAbstract
Relational database architecture has a significant impact on how data are managed, retrieved, and stored, making it essential for effective data management. Two key design strategies that influence the structure of a relational database are normalization and denormalization. Normalization organizes data into structured tables to eliminate redundancy and ensure data integrity. Although this approach simplifies updates, it may lead to performance degradation due to complex queries and frequent join operations. In contrast, denormalization improves performance by reducing or eliminating join operations, at the cost of increased data redundancy and storage requirements. This paper investigates the impact of these design strategies on database performance, with a focus on improving query efficiency by minimizing the number of joins required for data retrieval. Using SQL Server as the chosen RDBMS and applying it to a School Grades Management System, this study demonstrates practical implementations of normalized and denormalized schemas through structured queries. Furthermore, by presenting performance benchmarks supported by indexing optimization strategies, this work aims to guide database designers in selecting an appropriate design strategy that achieves an optimal balance between data integrity and system performance.
References
[1] B. Alathari, “The Comparative Analysis for Data Normalization Using User Interface Normal Form,” Int. J. Recent Trends Eng. Res., vol. 4, no. 3, pp. 59–65, 2018, doi: 10.23883/ijrter.2018.4097.qlzhe.
[2] M. Albarak, R. Bahsoon, I. Ozkaya, and R. Nord, “Managing Technical Debt in Database Normalization,” IEEE Trans. Softw. Eng., vol. 48, no. 3, pp. 755–772, 2022, doi: 10.1109/TSE.2020.3001339.
[3] R. Angles, M. Arenas-Salinas, R. García, and B. Ingram, “An optimized relational database for querying structural patterns in proteins,” Database, vol. 2024, no. 00, 2023, doi: 10.1093/database/baad093.
[4] B. Lemahieu, Seppe Broucke, Principles of Database Management, 2018, doi: 10.1017/9781316888773.
[5] A. N. Abyzov, “Balancing Data Normalization and Denormalization in Sports Competition Management Platforms: A Comparative Analysis,” Cyberleninka, 2023.
[6] Y. Jani, “Optimizing Database Performance for Large-Scale Enterprise Applications,” Oct. 2022, 2024, doi: 10.13140/RG.2.2.14180.59521.
[7] PhoenixNAP, “MySQL Performance Optimization - Day 5,” pp. 1–16, 2021.
[8] F. Zhou and Y. Gao, “Performance Study on Normalization and Denormalization in MES System Databases,” in 2024 Int. Conf. Intell. Comput. Data Mining (ICDM), IEEE, Sep. 2024, pp. 90–94, doi: 10.1109/ICDM63232.2024.10762243.
[9] Y. Zhang et al., “A fragmentation-aware redundancy elimination scheme for inline backup systems,” Future Gener. Comput. Syst., vol. 156, pp. 53–63, Jul. 2024, doi: 10.1016/j.future.2024.03.004.
[10] K. K. Tirupati, S. P. Singh, S. Nadukuru, S. Jain, and R. Agarwal, “Improving Database Performance with SQL Server Optimization Techniques,” Modern Dyn.: Math. Progressions, vol. 1, no. 2, pp. 450–494, Aug. 2024, doi: 10.36676/mdmp. v1.i2.32.
[11] E. Akadal and M. H. Satman, “A Novel Automatic Relational Database Normalization Method,” Acta Informatica Pragensia, vol. 11, no. 3, pp. 293–308, 2022, doi: 10.18267/j.aip.193.
[12] R. K. Rajendran and T. M. Priya, “Designing an Efficient and Scalable Relational Database Schema: Principles of Design for Data Modeling,” in The Software Principles of Design for Data Modeling, 2023, pp. 168–176, doi: 10.4018/978-1-6684-9809-5.ch013.
[13] B. Alathari, “The Comparative Analysis for Data Normalization Using User Interface Normal Form,” Int. J. Recent Trends Eng. Res., vol. 4, no. 3, pp. 59–65, 2018, doi: 10.23883/ijrter.2018.4097.qlzhe.
[14] V. B. Ramu, “Optimizing Database Performance: Strategies for Efficient Query Execution and Resource Utilization,” Int. J. Comput. Trends Technol., vol. 71, no. 7, pp. 15–21, 2023, doi: 10.14445/22312803/ijctt-v71i7p103.
[15] N. Singh, “Normalization in DBMS (Normal Forms),” May 2023, ResearchGate.
[16] N. Amato, “Mastering Database Normalization: A Comprehensive Exploration of Normal Forms,” ResearchGate, 2023.
[17] H. Köhler, “Finding Faithful Boyce–Codd Normal Form Decompositions,” in Proc. 2nd Int. Conf. Algorithmic Aspects Inf. Manag., Lecture Notes in Computer Science, Jun. 2006, doi: 10.1007/11775096_11.
[18] M. Fischer, P. Roessler, P. Sieben, J. Adamcic, C. Kirchherr, T. Sträubig, Y. Kaminsky, and F. Naumann, “BCNF* – From Normalized- to Star-Schemas and Back Again,” in Companion of the 2023 Int. Conf. on Management of Data (SIGMOD ’23), New York, NY, USA: ACM, 2023, pp. 103–106, doi: 10.1145/3555041.3589712.
[19] T. Akbar, “Normalization in Database and Its Uses in Cloud Computing,” ResearchGate, 2022.
[20] S. Shah, “A Systematic Method for On-The-Fly Denormalization of Relational Databases,” pp. 1–10, 2020.
[21] I. C. Saidu, M. Yusuf, F. C. Nemariyi, and A. C. George, “Indexing Techniques and Structured Queries for Relational Databases Management Systems,” J. Niger. Soc. Phys. Sci., vol. 6, no. 4, 2024, doi: 10.46481/jnsps.2024.2155.
[22] M. V. Chikkamannur, A. A., and Praveena, “Indexing Strategies for Performance Optimization of Relational Databases,” Int. Res. J. Eng. Technol., no. May, pp. 3801–3805, 2021.
[23] A. Anchlia, “Enhancing Query Performance Through Relational Database Indexing,” Int. J. Comput. Trends Technol., vol. 72, no. 8, pp. 130–133, 2024, doi: 10.14445/22312803/IJCTT-V72I8P119.
[24] H. Gadde, “AI-Assisted Decision-Making in Database Normalization and Optimization,” Int. J. Mach. Learn. Res. Cyber Secur. Artif. Intell., vol. 11, no. 01, pp. 230–259, 2020.
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