Problem Statement
An EdTech platform serving over 3 million student records was facing reliability, performance, and scaling challenges on its legacy server-based infrastructure.
The platform experienced:
- Slow performance during peak exam and enrollment periods
- Manual deployment processes causing downtime
- Difficulty scaling services independently
- Rising infrastructure costs due to inefficient resource usage
- Lack of structured data archival and backup policies
The company needed a highly available, scalable, and automated cloud environment capable of handling large data workloads, continuous product updates, and future user growth.
Our Approach
MetaCXO led the full cloud migration and modernization initiative, transforming the platform into a containerized, Kubernetes-based microservices architecture deployed on AWS.
Key Steps:
-
Infrastructure Design (AWS)
- Architected a multi-AZ Kubernetes cluster using Amazon EKS
- Designed secure VPC with private subnets, NAT gateways, and routing isolation
- Implemented IAM role-based access and fine-grained identity control
-
Containerization & Orchestration
- Containerized legacy applications using Docker
- Converted services to Helm charts for reproducible deployment
- Set up horizontal pod autoscaling for load-based performance
-
CI/CD Automation
- Implemented Git-based deployment workflow with GitHub Actions + ArgoCD
- Enabled zero-downtime rolling updates
- Introduced environment promotion workflows (dev → staging → prod)
-
Data Migration & Archival
- Migrated over 3M+ student data records to Amazon RDS (PostgreSQL)
- Set up S3-based archival for historical/low-access data to reduce storage costs
- Configured lifecycle policies for automatic archival & retention compliance
- Used AWS DMS + custom batching scripts for consistent, zero-loss migration
-
Observability & Performance Monitoring
- Integrated Prometheus + Grafana dashboards for performance visibility
- Configured AWS CloudWatch + Loki for centralized logs and alerting
- Implemented SLO/SLA tracking and auto-healing policies
Outcome
The platform successfully transitioned to a cloud-native architecture with:
- 99.9% uptime during high-traffic academic cycles
- 60% faster request response time after container-based optimization
- Fully automated deployments with zero-downtime releases
- Scalable Kubernetes workloads that auto-adjust resources based on demand
- Storage cost reduction from strategic archival of inactive data to S3 + Glacier
- Improved observability, monitoring, and operational reliability
The migration enabled the EdTech company to grow without infrastructure bottlenecks, support new feature rollouts faster, and operate at enterprise-grade stability and compliance levels.