Performance Benchmarking of Legacy Data Warehouse Platforms vs Cloud Data Warehouse Platforms for Large-Scale Analytical Workloads
Main Article Content
Abstract
This study presents a comprehensive performance benchmarking analysis comparing legacy on-premise data warehouse platforms with modern cloud-based data warehouse systems for large-scale analytical workloads. As enterprises transition toward scalable, elastic, and cost-efficient architectures, understanding the real-world performance differences between traditional and cloud environments becomes critical. The research evaluates query execution time, concurrency handling, workload scalability, storage throughput, cost-performance efficiency, and system reliability across representative workloads that include batch processing, complex analytical queries, and mixed query loads. Experimental results demonstrate that cloud data warehouses consistently outperform legacy platforms in elasticity, distributed compute optimization, and workload parallelization, while legacy systems still show strengths in predictable performance for stable workloads and tightly-governed environments. The study provides quantitative insights, highlights configuration and optimization factors influencing performance, and offers strategic recommendations for organizations planning modernization or hybrid migration.
Article Details
How to Cite
References
Inmon, W. H. (2005). Building the data warehouse (4th ed.). Wiley.
Kimball, R., & Ross, M. (2013). The data warehouse toolkit: The definitive guide to dimensional modeling (3rd ed.). Wiley.
Korhonen, J. J., & Ainamo, A. (2003). Redesigning the infrastructure for business intelligence. Communications of the Association for Information Systems, 11(1), 1–30.
Chaudhuri, S., Dayal, U., & Narasayya, V. (2011). An overview of business intelligence technology. Communications of the ACM, 54(8), 88–98.
Stonebraker, M., Abadi, D. J., DeWitt, D. J., Madden, S., Paulson, E., Pavlo, A., & Rasin, A. (2010). MapReduce and parallel DBMSs: Friends or foes? Communications of the ACM, 53(1), 64–71.
Abadi, D. J. (2009). Data management in the cloud: Limitations and opportunities. IEEE Data Engineering Bulletin, 32(1), 3–12.
Pavlo, A., Paulson, E., Rasin, A., Abadi, D. J., DeWitt, D. J., Madden, S., & Stonebraker, M. (2009). A comparison of approaches to large-scale data analysis. SIGMOD Proceedings, 165–178.
Elmore, A. J., Das, S., Agrawal, D., & Abbadi, A. E. (2011). Zephyr: Live migration in shared nothing databases for elastic cloud platforms. Proceedings of the 2011 ACM SIGMOD, 301–312.
Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Patterson, D., & Zaharia, M. (2010). A view of cloud computing. Communications of the ACM, 53(4), 50–58.
Lu, H., Holubova, I., & Morfonios, K. (2019). Survey of graph database performance on the cloud. Journal of Big Data, 6(1), 1–30.
Marz, N., & Warren, J. (2015). Big data: Principles and best practices of scalable real-time data systems. Manning.
Davenport, T. H. (2014). Big data at work: Dispelling the myths, uncovering the opportunities. Harvard Business Review Press.
Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., & Khan, S. U. (2015). The rise of big data on cloud computing: Review and open issues. Information Systems, 47, 98–115.
Rajan, C. A., & Narayanan, V. (2014). Data warehousing in cloud: A survey. International Journal of Computer Applications, 108(12), 1–7.
Jarke, M., & Vassiliou, Y. (1997). Foundations of data warehousing. Database and Expert Systems Applications, 1–10.
Miller, G., & von Laszewski, G. (2017). Benchmarking cloud systems. Cloud Computing Journal, 3(2), 45–56.
Borkar, V., Carey, M. J., & Li, C. (2012). Inside big data management: Ogres, onions, or parfaits? Proceedings of the 15th International Conference on Extending Database Technology, 3–14.
White, T. (2015). Hadoop: The definitive guide (4th ed.). O’Reilly Media.
Dean, J., & Ghemawat, S. (2008). MapReduce: Simplified data processing on large clusters. Communications of the ACM, 51(1), 107–113.
Zaharia, M., Chowdhury, M., Das, T., Dave, A., Ma, J., & Rosen, J. (2012). Resilient distributed datasets: A fault-tolerant abstraction for in-memory cluster computing. Proceedings of the 9th USENIX Symposium on Networked Systems Design and Implementation, 15–28.