A Data Migration Platform That Never Stops the Business

  • Authors

    • Yuga Sai Kiran Reddy Sudini Director Of Engineering and operations at Public Partnerships LLC, USA. Author

    DOI:

    https://doi.org/10.67228/30715628/IJMIET-2024PII5R7K

    Published 11-06-2024

  • Data Migration, Zero-Downtime Migration, Legacy Modernization, Change Data Capture, Continuous Data Synchronization, Data Transformation, Data Validation, Data Reconciliation, Business Continuity, Enterprise Systems, Cloud Migration, Application Modernization

    Issue

    Vol. 7 No. 2 (2024)

    Section

    Articles

  • Abstract

    Legacy data migration is a critical challenge for organizations operating business systems that require continuous availability, uninterrupted production services, and concurrent application development. Conventional migration approaches often depend on system downtime or operational freezes, creating risks to business continuity, data consistency, and application reliability. This paper presents a continuous data migration platform designed to migrate legacy application data into a modern target solution while maintaining uninterrupted production operations and supporting ongoing development activities. The proposed approach integrates incremental data synchronization, change data capture, transformation pipelines, validation mechanisms, reconciliation processes, and controlled cutover strategies to enable migration beneath an actively evolving application environment. The platform continuously captures changes from the source system, processes and synchronizes data with the target environment, and validates data integrity throughout the migration lifecycle. This architecture reduces dependency on extended maintenance windows and enables organizations to progressively transition from legacy platforms without disrupting business operations. The proposed framework also supports parallel development and testing, allowing application teams to validate migrated data while production workloads remain operational. The study highlights how continuous synchronization, automated validation, and phased migration strategies can improve data consistency, migration reliability, operational continuity, and modernization efficiency. The proposed platform provides a practical architectural foundation for zero-downtime data migration in large-scale enterprise environments.

  • References

    [1] Polamreddy, Vivekananda Reddy. "Engineering Reversible Enterprise Data Migrations: A Phased Rollout Framework for Financially Critical Retail Platforms." International Journal of Computer Technology and Electronics Communication 4.2 (2021): 3414-3427.

    [2] Kansara, Maheshbhai. "Cloud migration strategies and challenges in highly regulated and data-intensive industries: A technical perspective." International Journal of Applied Machine Learning and Computational Intelligence 11.12 (2021): 78-121.

    [3] Elmore, Aaron J., et al. "Zephyr: live migration in shared nothing databases for elastic cloud platforms." Proceedings of the 2011 ACM SIGMOD International Conference on Management of data. 2011.

    [4] Marston, Sean, et al. "Cloud computing—The business perspective." Decision support systems 51.1 (2011): 176-189.

    [5] Rahman, K. Sabeel, and Kathleen Thelen. "The rise of the platform business model and the transformation of twenty-first-century capitalism." Politics & society 47.2 (2019): 177-204.

    [6] Kagermann, Henning. "Change through digitization—Value creation in the age of Industry 4.0." Management of permanent change. Wiesbaden: Springer Fachmedien Wiesbaden, 2014. 23-45.

    [7] Madakam, Somayya, Rajesh M. Holmukhe, and Durgesh Kumar Jaiswal. "The future digital work force: robotic process automation (RPA)." JISTEM-Journal of Information Systems and Technology Management 16 (2019): e201916001.

    [8] Morris, Johny. Practical data migration. BCS, The Chartered Institute, 2012.

    [9] Van Alstyne, Marshall W., Geoffrey G. Parker, and Sangeet Paul Choudary. "Pipelines, platforms, and the new rules of strategy." Harvard business review 94.4 (2016): 54-62.

    [10] Hussein, Abou_el_ela Abdou. "Data migration need, strategy, challenges, methodology, categories, risks, uses with cloud computing, and improvements in its using with cloud using suggested proposed model (DMig 1)." Journal of Information Security 12.1 (2021): 79-103.

    [11] Weill, Peter, and Michael Vitale. Place to space: Migrating to eBusiness Models. Harvard Business Press, 2001.

    [12] Helmond, Anne. "The platformization of the web: Making web data platform ready." Social media+ society 1.2 (2015): 2056305115603080.

    [13] Weinhardt, Christof, et al. "Cloud computing–a classification, business models, and research directions." Business & Information Systems Engineering 1.5 (2009): 391-399.

    [14] Azeroual, Otmane, and Meena Jha. "Without data quality, there is no data migration." Big Data and Cognitive Computing 5.2 (2021): 24.

    [15] Evans, David S., Andrei Hagiu, and Richard Schmalensee. Invisible engines: How software platforms drive innovation and transform industries. The MIT Press, 2008.

    [16] Taluri, R. (2022). Cloud Data Engineering Strategies for Large-Scale Financial Data Integration and Intelligent Corporate Performance Reporting. International Journal of Emerging Research in Engineering and Technology, 3(4), 176-188. https://doi.org/10.63282/3050-922X.IJERET-V3I4P119

    [17] Veershetty, G. (2019). From Legacy Back Office to Intelligent Utility Enterprise a Practitioner Case Study of SAP Cloud Transformation and Utility IT Landscape Modernization. American International Journal of Computer Science and Technology, 1(1), 23-27. https://doi.org/10.63282/3117-5481/AIJCST-V1I1P103

  • Downloads

Similar Articles

91-100 of 114

You may also start an advanced similarity search for this article.