Cloud-Native Data Migration Frameworks for Modernizing Legacy Warehouses into Cloud Platforms

Pramod Raja Konda

Abstract


The transition from legacy data warehouses to cloud platforms has become a strategic imperative for organizations seeking scalability, resilience, and advanced analytics capabilities. However, migrating decades-old systems to cloud-native environments introduces challenges related to interoperability, data consistency, security, performance optimization, and minimal operational disruption. This paper presents a comprehensive examination of cloud-native data migration frameworks designed to modernize legacy warehouses through automated orchestration, containerization, parallel processing, and metadata-driven transformation. The proposed framework integrates ETL modernization, schema harmonization, real-time replication, and cloud-native services to ensure seamless migration across hybrid and multi-cloud ecosystems. Through analysis of existing models and architectural patterns, the study highlights the role of microservices, serverless computing, and distributed storage in accelerating modernization efforts. Experimental insights demonstrate improvements in migration speed, data accuracy, and system reliability. The findings contribute to the growing body of knowledge on digital transformation by offering organizations a structured pathway to adopt agile, elastic, and cost-optimized cloud-native data architectures

Full Text:

PDF

References


Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R., Konwinski, A., Lee, G., Patterson, D., Rabkin, A., Stoica, I., & Zaharia, M. (2010). A view of cloud computing. Communications of the ACM, 53(4), 50–58.

Bailey, J., Gudivada, V., & Pattabiraman, B. (2014). Big data analytics: Challenges and opportunities. IEEE International Conference on Big Data, 17–24.

Dean, J., & Ghemawat, S. (2008). MapReduce: Simplified data processing on large clusters. Communications of the ACM, 51(1), 107–113.

Dikaiakos, M. D., Katsaros, D., Mehra, P., Pallis, G., & Vakali, A. (2009). Cloud computing: Distributed internet computing for IT and scientific research. IEEE Internet Computing, 13(5), 10–13.

Erl, T. (2014). Cloud computing: Concepts, technology & architecture. Prentice Hall.

Gantz, J., & Reinsel, D. (2011). Extracting value from chaos. IDC iView Report.

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 research issues. Information Systems, 47, 98–115.

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.

Marinescu, D. C. (2013). Cloud computing: Theory and practice. Morgan Kaufmann.

Mell, P., & Grance, T. (2011). The NIST definition of cloud computing. NIST Special Publication 800-145.

Microsoft. (2014). Data migration best practices. Microsoft Press.

Oracle Corporation. (2013). Oracle data integration: ETL and data migration guide. Oracle Technical Publications.

Patterson, D. A., Gibson, G., & Katz, R. H. (1988). A case for redundant arrays of inexpensive disks (RAID). ACM SIGMOD, 109–116.

Rivest, R. (1992). The MD5 message-digest algorithm. RFC Editor.

Schadt, E. E., Linderman, M. D., Sorenson, J., Lee, L., & Nolan, G. P. (2010). Cloud and heterogeneous computing for big data analytics. Nature Reviews Genetics, 11(9), 615–628.

Stonebraker, M., Abadi, D. J., DeWitt, D., Madden, S., Paulson, E., Pavlo, A., & Rasin, A. (2010). MapReduce and parallel DBMSs: Friends or foes? Communications of the ACM, 53(1), 64–71.

Toomey, D. (2014). Data migration: A practical guide to transforming enterprise data. Technics Publications.

Weber, R. H. (2010). Internet of Things – New security and privacy challenges. Computer Law & Security Review, 26(1), 23–30.

Zaharia, M., Chowdhury, M., Franklin, M. J., Shenker, S., & Stoica, I. (2010). Spark: Cluster computing with working sets. USENIX HotCloud, 1–7.


Refbacks

  • There are currently no refbacks.


Copyright (c) 2025 International Journal of Sustainable Development in Computing Science

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

A Double-Blind Peer Reviewed Journal