From Legacy to Modern Systems: An Enterprise-ready Workflow for Database Migrations
DOI:
https://doi.org/10.5753/jidm.2026.5730Keywords:
Database Migration, Migration Strategies, WorkflowAbstract
Data migration across different database management systems (DBMSs) is a critical task for companies aiming to modernize their infrastructure, reduce risks, and improve operational efficiency. This paper presents a workflow to assist in conducting migrations, from planning to post-migration. The objective is to provide a roadmap for migrations, reducing execution time, mitigating risks, and ensuring data integrity and security. The proposed approach was validated in a real company scenario, demonstrating its feasibility and effectiveness. This article makes a practical contribution to the data migration area, providing a validated roadmap that assists companies in conducting migrations efficiently and safely.
Downloads
References
Azeroual, O. and Jha, M. (2021). Without data quality, there is no data migration. Big Data and Cognitive Computing, 5(2):24. DOI: 10.3390/bdcc5020024.
Boddapati, V. N., Sarisa, M., Reddy, M. S., Sunkara, J. R., Rajaram, S. K., Bauskar, S. R., and Polimetla, K. (2022). Data migration in the cloud database: A review of vendor solutions and challenges. Available at SSRN 4977121.
Carneiro, V., Moraes, G., and Brayner, A. (2024). I-datamig: Uma ferramenta inteligente para migração eficiente de bancos de dados. In Anais Estendidos do XXXIX Simpósio Brasileiro de Bancos de Dados, pages 89–94, Porto Alegre, RS, Brasil. SBC. DOI: 10.5753/sbbdestendido.2024.240758.
Elamparithi, M. and Anuratha, V. (2015). A review on database migration strategies, techniques and tools. World Journal of Computer Application and Technology, 3(3):41–48. DOI: 10.13189/wjcat.2015.030301.
Ellison, M., Calinescu, R., and Paige, R. F. (2018). Evaluating cloud database migration options using workload models. Journal of Cloud Computing, 7:1–18. DOI: https://doi.org/10.1186/s13677-018-0108-5.
Fuaad, H., Ibrahim, A., Majed, A., and Asem, A. (2018). A survey on distributed database fragmentation allocation and replication algorithms. Current Journal of Applied Science and Technology, 27(2):1–12. DOI: 10.9734/CJAST/2018/37079.
Google Cloud (2024). Database migration: Concepts and principles (part 1). https: //cloud.google.com/architecture/ database-migration-concepts-principles-part-1. Acessado em: 2024-05-29.
Haller, K. (2009). Towards the industrialization of data migration: concepts and patterns for standard software implementation projects. In Advanced Information Systems Engineering: 21st International Conference, CAiSE 2009, Amsterdam, The Netherlands, June 8-12, 2009. Proceedings 21, pages 63–78. Springer. DOI: https://doi.org/10.1007/978-3-642-02144-210.
Hussein, A. A. et al. (2021). 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(01):79. DOI: 10.4236/jis.2021.121004.
Karnitis, G. and Arnicans, G. (2015). Migration of relational database to document-oriented database: Structure denormalization and data transformation. In 2015 7th international conference on computational intelligence, communication systems and networks, pages 113–118. IEEE. DOI: 10.1109/CICSyN.2015.30.
Magalhaes, A., Monteiro, J. M., and Brayner, A. (2021). Main memory database recovery: A survey. ACM Comput. Surv., 54(2). DOI: 10.1145/3442197.
Mayr, H. C. and Thalheim, B. (2021). The triptych of conceptual modeling: A framework for a better understanding of conceptual modeling. Software and Systems Modeling, 20(1):7–24. DOI: 10.1007/s10270-020-00836-z.
Monteiro Filho, J. M. S. and Brayner, A. R. A. (2013). Sintonia e auto-sintonia de bancos de dados. In Medeiros, C. B., editor, Atualizações em Informática, volume 1, pages 141–206. Ed. Edufal, Maceió.
Moraes, G., Misael, V., and Brayner, A. (2024). Gerenciamento de migração de dados: Um workflow eficiente para empresas. In Anais do XXXIX Simpósio Brasileiro de Bancos de Dados, pages 841–847, Porto Alegre, RS, Brasil. SBC. DOI: 10.5753/sbbd.2024.240760.
Nan, Z. and Bai, X. (2019). The study on data migration from relational database to graph database. In Journal of Physics: Conference Series, volume 1345, page 022061. IOP Publishing. DOI: 10.1088/1742-6596/1345/2/022061.
Neto, P. S., Neto, J. R., Júnior, F. R., and Oliveira, P. (2013). Requisitos para ferramentas de migração de dados. In Anais do IX Simpósio Brasileiro de Sistemas de Informação, pages 887–898, Porto Alegre, RS, Brasil. SBC. DOI: 10.5753/sbsi.2013.5749.
Ozsu, M. T. and Valduriez, P. (2001). Principios de sistemas da bancos de dados distribuidos. Campus.
Singhal, B. and Aggarwal, A. (2022). Etl, elt and reverse etl: a business case study. In 2022 Second International Conference on Advanced Technologies in Intelligent Control, Environment, Computing & Communication Engineering (ICATIECE), pages 1–4. IEEE. DOI: 10.1109/ICATIECE56365.2022.10046997.
Thalheim, B. and Wang, Q. (2013). Data migration: A theoretical perspective. Data & Knowledge Engineering, 87:260– 278. DOI: https://doi.org/10.1016/j.datak.2012.12.003.
Wang, G., Jia, Z., and Xue, M. (2014). Data migration model and algorithm between heterogeneous databases based on web service. Journal of Networks, 9(11):3127. DOI: 10.4304/jnw.9.11.3127-3134.

