Zero-downtime cloud migration and optimization
A high-growth e-commerce retailer was losing sales during peak events because their on-premise servers could not handle the traffic. We migrated them to AWS with zero downtime, auto-scaling, and a more efficient cost profile.
Zero
downtime cutover
Auto-scaling
for peak sales
Right-sized
cloud costs
The Challenge
What High-growth e-commerce retailer was facing
The on-premise setup was a fixed pair of application servers and a single primary database with a cold standby - no ability to scale up automatically for traffic spikes.
During flash sales and holiday peaks, response times climbed to the point where the site became effectively unusable, and the business lost sales as a result.
The ops team managed infrastructure manually: provisioning new capacity took days, and there was no infrastructure-as-code, no automated deployments, and no centralized logging.
The product catalog database had grown large with no partitioning strategy, and full-table scans on catalog queries were a primary performance bottleneck.
Our Approach
How we solved it
We designed a phased migration plan: first the static assets and CDN layer, then the application servers, then the database - each phase validated independently before proceeding to the next.
The application was containerized with Docker and deployed on ECS Fargate with auto-scaling policies tied to CPU utilization and request count, so capacity spins up automatically during traffic spikes.
We migrated the PostgreSQL database to RDS with read replicas, implemented table partitioning on the product catalog and order history tables, and added Elasticsearch for product search - taking catalog queries from slow full-table scans to fast indexed lookups.
CloudFront CDN serves static assets and product images from global edge locations, reducing origin load and cutting page load times for international customers.
Key Features
What we delivered
Zero-Downtime Migration
The entire platform was migrated from on-premise to AWS without downtime, using DNS-based traffic shifting and parallel environments.
Auto-Scaling Infrastructure
ECS Fargate containers scale out automatically based on traffic, absorbing peak sales load without manual intervention.
Database Optimization
Table partitioning, read replicas, and Elasticsearch integration take product queries from slow full-table scans to fast indexed lookups.
CDN and Edge Caching
CloudFront serves static assets and product images from global edge locations, reducing origin load and speeding up international page loads.
Infrastructure as Code
The entire AWS environment is defined in Terraform, enabling reproducible deployments, disaster recovery, and environment parity between staging and production.
Centralized Observability
CloudWatch, X-Ray, and custom dashboards provide real-time visibility into application performance, error rates, and infrastructure costs.
Our Process
How we got there
Infrastructure Audit
We documented every server, service, cron job, and integration in the on-premise environment, identifying dependencies and migration priorities.
Containerization
We containerized the application with Docker, created CI/CD pipelines with GitHub Actions, and validated the containerized app against the production workload in a staging environment.
CDN and Static Asset Migration
We moved all static assets to S3 + CloudFront first, reducing origin server load immediately and validating the CDN configuration under real traffic.
Application Migration
We deployed the containerized application on ECS Fargate, configured auto-scaling, and shifted traffic from on-premise to AWS using weighted DNS routing.
Database Migration
We used AWS DMS for continuous replication from the on-premise database to RDS, validated data integrity, then cut over read traffic to replicas and finally the primary.
Results
What the solution delivers
Zero-Downtime Cutover
The migration from on-premise to AWS was executed with no downtime, using parallel environments and DNS-based traffic shifting validated by synthetic monitoring.
Auto-Scaling for Peak Sales
Capacity now scales out automatically during flash sales and holiday peaks instead of being capped by fixed on-premise hardware.
Right-Sized, Efficient Costs
Moving to managed AWS services with right-sizing and reserved instances removed on-premise hardware overhead and made spend track actual usage.
Technology Stack
What we used
Cloud Platform
Infrastructure as Code
Data & Search
Monitoring
Migration Tools
Business Impact
The bigger picture
With auto-scaling in place, the retailer can handle peak sales events without the slowdowns that previously cost them orders, and infrastructure spend now tracks actual usage instead of paying for idle hardware. The CI/CD pipeline and infrastructure-as-code foundation also let the engineering team ship changes far more frequently than the old manual process allowed.
What this project demonstrates
- Shows how we execute a phased, zero-downtime migration off on-premise infrastructure.
- Demonstrates auto-scaling on ECS Fargate to absorb unpredictable peak traffic.
- Illustrates database and search optimization with partitioning, read replicas, and Elasticsearch.
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