A Reconciliation-Driven Methodology for Legacy-to-Cloud Data Warehouse Migration: A Framework for the Enterprise Reporting Platform
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V4I3P121Keywords:
Cloud Data Migration, Snowflake Data Warehouse, AWS Glue ETL, Data Reconciliation, Government Public Sector Analytics, Legacy System Modernization, Data Quality ValidationAbstract
Government health-benefit systems accumulate decades of fragmented data across relational databases, flat files, and unstructured documents, creating risk when modernizing to cloud-native analytics platforms. This article presents a reconciliation-driven migration methodology applied to the organization Enterprise Reporting Platform, migrating legacy Microsoft SQL Server data, CSV/Excel files, and PDF-sourced records into a Snowflake warehouse on AWS infrastructure. Existing approaches prioritize extraction throughput over validation, leaving completeness and rule-conformance checks as a manual, post-hoc activity-a gap that is costly in regulated, multi-source government environments spanning domains such as CKMMD, MMD, TMED, TPEAS, DSNS, and Communications/IC Request processing. The proposed methodology embeds profiling, staged extraction, auditable loading, and rule-based reconciliation as concurrent, automated pipeline stages rather than sequential afterthoughts, orchestrated through AWS Glue, staged in S3 and RDS, and validated within Snowflake prior to Power BI consumption. Results across six domains show completeness improving from a baseline of 91.4% to 99.6%, a 73% reduction in manual validation effort, and load cycle time reduced by 61% versus a sequential baseline. These results indicate embedding reconciliation directly into the pipeline-rather than as a terminal audit step-produces measurably higher data trustworthiness at lower operational cost, offering a transferable pattern for other state-level health and human-services modernization programs.
References
[1] Forsgren, N., Humble, J., & Kim, G. (2018). Accelerate: The science of lean software and DevOps. IT Revolution Press.
[2] Kleppmann, M. (2017). Designing data-intensive applications. O'Reilly Media.
[3] Dehghani, Z. (2019). How to move beyond a monolithic data lake to a distributed data mesh. MartinFowler.com Technical Articles.
[4] Stonebraker, M., & Ilyas, I. F. (2018). Data integration: The current status and the way forward. IEEE Data Engineering Bulletin, 41(2), 3–9.
[5] Abadi, D., et al. (2020). The Seattle report on database research. ACM SIGMOD Record, 48(4), 44–53.
[6] Hellerstein, J. M., et al. (2017). Ground: A data context service. Proceedings of the 8th Biennial Conference on Innovative Data Systems Research (CIDR).
[7] Chandra Sekhar Oleti. (2022). Serverless Intelligence: Securing J2ee-Based Federated Learning Pipelines on AWS. International Journal of Computer Engineering and Technology (IJCET), 13(3), 163-180. https://iaeme.com/MasterAdmin/Journal_uploads/IJCET/VOLUME_13_ISSUE_3/IJCET_13_03_017.pdf
[8] Sambasivan, N., et al. (2021). "Everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. Proceedings of the CHI Conference on Human Factors in Computing Systems.
[9] Stonebraker, M., & Çetintemel, U. (2018). "One size fits all": An idea whose time has come and gone. Proceedings of the IEEE International Conference on Data Engineering.
[10] Sandeep Kamadi, " Adaptive Federated Data Science & MLOps Architecture: A Comprehensive Framework for Distributed Machine Learning Systems" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 8, Issue 6, pp.745-755, November-December-2022. Available at doi : https://doi.org/10.32628/CSEIT22555
[11] Sivaramakrishnan Narayanan (2022). Transforming Cybersecurity with AI-driven Dashboards: A Cloud-Native Implementation Framework for Real-Time Threat Detection and Automated Response. International Journal of Future Innovative Science and Technology (IJFIST) , Vol. 5 No. 5 (2022): International Journal of Future Innovative Science and Technology (IJFIST) , pp. 9207-9217. https://doi.org/10.15662/IJFIST.2022.0505004
[12] Meesala, A. (2022). Adaptive Spread Anomaly Intelligence Framework (ASAIF): A cloud-native AI framework for real-time bid-ask spread anomaly detection and cross-venue liquidity risk intelligence. International Journal of Future Innovative Science and Technology, 5(6). https://doi.org/10.15662/IJFIST.2022.0506007
[13] National Institute of Standards and Technology. (2018). Framework for improving critical infrastructure cybersecurity, Version 1.1. NIST.
[14] U.S. Government Accountability Office. (2019). Data governance: Selected agencies need to address data quality issues (GAO-19-460). GAO.
[15] Oleti, Chandra Sekhar. (2022). The future of payments: Building high-throughput transaction systems with AI and Java Microservices. World Journal of Advanced Research and Reviews. 16. 1401-1411. 10.30574/wjarr.2022.16.3.1281.
[16] Sandeep Kamadi, " Risk Exception Management in Multi-Regulatory Environments: A Framework for Financial Services Utilizing Multi-Cloud Technologies" International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN: 2456-3307, Volume 7, Issue 5, pp.350-361, September-October-2021. Available at doi : https://doi.org/10.32628/CSEIT217560
[17] Centers for Medicare & Medicaid Services. (2019). Medicaid Information Technology Architecture (MITA) 3.0 framework. CMS.gov.
[18] Meesala, L. K. (2022). Autonomous cyber risk quantification and adaptive defense in financial systems: A graph intelligence and reinforcement learning framework. World Journal of Advanced Research and Reviews, 16(3), 1489–1496. https://doi.org/10.30574/wjarr.2022.16.3.1354
[19] Redman, T. C. (2018). Getting in front on data: Who does what. Technics Publications.
[20] Kamadi, Sandeep. (2022). Proactive cybersecurity for enterprise APIs: leveraging ai-driven intrusion detection systems in distributed java environments. International journal of research in computer applications and information technology. 5. 34-52. 10.34218/IJRCAIT_05_01_004.
[21] Lakshmi Kiran Meesala "AI-Driven Cyber Defense: A Hybrid Deep Learning Framework for Real-Time Threat Prediction and Response" International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 10, Issue 2, pp.916-930, March-April-2023. Available at doi : https://doi.org/10.32628/IJSRSET235845
[22] Loshin, D. (2017). Data quality and master data management: A practitioner's perspective. Information Management Journal, 51(3), 22–28.
[23] Kamadi, Sandeep. (2022). AI-powered rate engines: modernizing financial forecasting using microservices and predictive analytics. International journal of computer engineering & technology. 13. 220-233. 10.34218/IJCET_13_02_024.
[24] Arun Meesala , " A Distributed Kafka-Centric Framework for High-Throughput Mid-Price Computation and Intelligent Time-Series Persistence in Financial Clouds" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 9, Issue 2, pp.998-1006, March-April-2023. Available at doi : https://doi.org/10.32628/CSEIT2342442
[25] Cloud Security Alliance. (2017). Security guidance for critical areas of focus in cloud computing, v4.0. CSA.
[26] Ross, R., et al. (2020). Security and privacy controls for information systems and organizations (NIST SP 800-53 Rev. 5). NIST.
