Technical Program Manager – System Design

Migrating enterprise workloads to AWS: How would you approach migrating a large, mission-critical customer workload to AWS Autonomous Database with zero downtime?

The fastest way to guarantee a customer never notices a mission-critical database migration is to make the actual cutover the least risky part of the project, not the biggest. AWS's closest analog to "Autonomous Database" is Amazon Aurora, and the entire strategy is built around one mechanism: AWS DMS replicates every change from the source to Aurora continuously, for as long as it takes to prove the target trustworthy — so cutover shrinks from a multi-hour migration event into a seconds-long, rehearsed traffic switch.

  1. 1.Assess & classify the workload — baseline transactions-per-second, peak concurrency, and the RPO/RTO the business actually requires, map every dependency that connects to the source, and run the AWS Schema Conversion Tool (SCT) and DMS Fleet Advisor to flag anything Aurora handles differently.
  2. 2.Design the target Aurora architecture sized for production load on day one — Aurora Serverless v2 ACUs or provisioned instance classes against measured peak concurrency, Multi-AZ Aurora Replicas for high availability, and security parity (VPC, KMS, IAM auth) with the source.
  3. 3.Stand up real-time replication — an AWS DMS full-load task moves the bulk of existing data without pausing source writes, then a DMS CDC task keeps Aurora trailing the source by seconds, with replication lag tracked continuously via CloudWatch.
  4. 4.Run a parallel shadow-validation phase — mirror production read traffic to an Aurora Replica and diff results field-by-field, run DMS's built-in data-validation checksums on a recurring schedule, and benchmark performance against SLAs using RDS Performance Insights.
  5. 5.Execute the cutover — briefly pause writes on the source, confirm DMS replication lag has drained to zero, repoint traffic via Route 53 or RDS Proxy, and keep the source live and restorable through a defined rollback window.
  6. 6.Stabilize and decommission — run an elevated hypercare window watching Aurora's Performance Insights and CloudWatch, decommission the source only once the rollback window passes with zero incidents, and capture the runbook for the next migration.

This holds up for a mission-critical workload specifically because the riskiest operation — moving the data — happens entirely in the background while the source keeps serving 100% of production traffic; the only customer-facing moment is a traffic switch that's already been rehearsed and is trivially reversible.