Intelligent Zero-Downtime Database Migration for Regulated Financial Systems: DMS-PredictLagNet - An Empirical Framework Using AWS DMS, Aurora, and Predictive CDC Lag Monitoring

Ajay Devineni

doi:10.36948/ijfmr.2026.v08i02.74175

Intelligent Zero-Downtime Database Migration for Regulated Financial Systems: DMS-PredictLagNet - An Empirical Framework Using AWS DMS, Aurora, and Predictive CDC Lag Monitoring

Author(s)	Ajay Devineni
Country	United States
Abstract	The migration of mission-critical database systems in regulated financial services environments represents one of the highest-risk operations in cloud engineering. These migrations combine extreme data volume constraints — tables containing billions of rows — with zero tolerance for data loss, narrow cutover windows measured in hours, and regulatory compliance requirements spanning SOC 2, PCI DSS, and related frameworks. This paper presents DMS-PredictLagNet, an empirical framework for achieving intelligent, zero-downtime database migration using AWS Database Migration Service (DMS) and Amazon Aurora, validated against production migrations I executed across multiple credit union banking environments in a regulated financial services context. Central to the framework is a predictive Change Data Capture (CDC) lag monitoring system built on Holt-Winters time-series forecasting, which forecasts replication lag 30 minutes ahead of threshold breach and triggers autonomous DMS scaling through AWS Lambda before disruption occurs. To handle extreme data volumes within a constrained eight-hour cutover window, I deployed two dedicated DMS replication instances on the latest available instance type with high-memory configurations: one instance dedicated exclusively to CDC replication for the single billion-row table, and a second instance handling the full load and CDC for all remaining tables. This isolation ensured that lag accumulation on the high-volume table could not starve replication throughput for the rest of the estate. The migrations achieved zero data loss, met the target cutover window, and produced a 41% query performance improvement post-migration to Aurora. This paper presents the full technical architecture, the predictive lag monitoring methodology, post-CDC validation scripting approach, and the deployment considerations for SOC 2 regulated environments — offered as a reproducible contribution to the cloud engineering and SRE communities.
Keywords	AWS Database Migration Service, Amazon Aurora, Change Data Capture, CDC Lag Monitoring, Holt-Winters Forecasting, Zero-Downtime Migration, Parallel DMS Replication, Financial Services Cloud, SOC 2 Compliance, AWS Transit Gateway, Site-to-Site VPN, Schema Conversion Tool, Data Validation.
Field	Engineering
Published In	Volume 8, Issue 2, March-April 2026
Published On	2026-03-13
DOI	https://doi.org/10.36948/ijfmr.2026.v08i02.74175

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Intelligent Zero-Downtime Database Migration for Regulated Financial Systems: DMS-PredictLagNet - An Empirical Framework Using AWS DMS, Aurora, and Predictive CDC Lag Monitoring

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