Dallas, TX, USA.
Research Article
World Journal of Advanced Research and Reviews, 2023, 19(01), 1569-1579
Received on 09 June 2023; revised on 22 July 2023; accepted on 26 July 2023
This paper provides a detailed comparative analysis of relational and dimensional data modeling strategies, utilizing AWS Cloud technologies to enhance scalability and performance. Relational data models are traditionally used for transactional systems, while dimensional models are more suited for analytical processing and business intelligence tasks. In this study, we used Amazon Redshift and AWS Glue to test the scalability of both strategies on large datasets. We evaluated the performance of each model by comparing query execution times, storage efficiency, and data accessibility. Results showed that dimensional modeling outperformed relational models in terms of query speed, with a 40% improvement in execution time for complex business intelligence queries. However, relational models demonstrated better efficiency in managing transactional data with a lower error margin. By leveraging AWS technologies, both models scaled efficiently, but dimensional models provided more flexibility in accommodating growth in data volume. This paper concludes that AWS cloud technologies can significantly improve the scalability of both relational and dimensional data models, but the choice of model should align with the specific data processing needs of the organization. This comparison provides practical insights into the strengths and weaknesses of each strategy, helping businesses optimize their data management processes.
AWS; Data Modeling; Relational; Dimensional; Scalability
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Rama Krishna Jujjavarapu. Employing AWS cloud technologies for enhanced scalability in data modeling: A comparative analysis of relational versus dimensional strategies. World Journal of Advanced Research and Reviews, 2023, 19(1), 1569-1579. Article DOI: https://doi.org/10.30574/wjarr.2023.19.1.1410