Zero Data Loss Migration: Moving Billions of Rows from SQL Server to Aurora RDS — Architecture, Predictive CDC Monitoring & Lessons from Production

Migrating a live financial database with billions of rows, zero tolerance for data loss, and a strict cutover window is not a data transfer problem. It is a resource isolation problem, a risk prediction problem, and a compliance documentation problem — all running simultaneously. This article documents the architecture and lessons from a production SQL Server → AWS Aurora RDS migration I executed across multiple credit union banking environments. The core contribution is a framework I built called DMS-PredictLagNet — combining parallel DMS instance isolation with Holt-Winters predictive CDC lag forecasting for autonomous scaling. The Challenge The source environment was on-premises SQL Server across two separate data centers. Hundreds of tables. Two tables with billions of rows each. Continuous live transaction traffic — no maintenance window available. SOC 2 Type II and PCI DSS compliance required throughout. The hardest constraint: cutover had to happen within a documented change win