Migrating from Heroku to AWS

The team protects data integrity through pre-cutover validation, controlled synchronisation, and a clear rollback plan. Before the cutover, the AWS environment runs in parallel while Heroku remains the source of truth. The team synchronises data using replication or export/import and verifies consistency with row counts, checksums, and application-level tests. During the final sync, the team pauses writes or queues them briefly to prevent drift. The rollback path is defined in advance. Heroku stays unchanged until the AWS environment proves stable in production. If issues appear after cutover, traffic is redirected back to Heroku immediately using the last consistent dataset.

Near-zero downtime is achieved through parallel environments, controlled traffic switching, and pre-cutover validation. The AWS environment runs in parallel with Heroku and stays fully production-ready before cutover. Data and configuration are synchronised ahead of time, and the team validates application behaviour using health checks, synthetic tests, and real production-like traffic. Traffic is switched using DNS or load-balancer controls in a staged manner. The team monitors error rates, latency, and logs in real time. Only after the AWS environment proves stable is traffic fully moved, keeping any disruption to a short, predictable window.

The setup replaces Heroku’s abstractions with a least-privilege, role-based IAM model aligned with AWS best practices. Access is split by role and environment, not by shared credentials. Applications use IAM roles instead of static secrets, and services receive only the permissions they need to operate. Human access is limited to defined roles with MFA and time-bound permissions. Network isolation through VPCs, security groups, and private subnets reduces the attack surface. Centralised logging and monitoring track all access and changes, providing full audit visibility without increasing operational risk.

The workflow shifts from “push to Heroku and it deploys” to a pipeline-driven release with explicit stages and approvals. Developers still push code to Git, but deployments run through CI/CD stages: build, tests, security checks, and deployment to dev/stage before production. The pipeline promotes the same artefact across environments, which makes releases more repeatable and easier to audit. The team also separates configuration from code more strictly (per-environment parameters and secrets management) and adds controlled rollout options such as blue/green or canary deployments.