Legacy ERP Modernization:
From Slow to Scalable
A client project where I transformed a struggling monolithic ERP into a cloud-native, production-ready system — achieving 90% performance improvement and 99.9% uptime.
The Challenge
Client's Initial State
- Monolithic architecture, single database
- API response time: 2-3 seconds
- No caching — every request hits DB
- Manual deployment (error-prone)
- Zero observability in production
- No audit trail for compliance
My Role
Working as Fullstack Engineer at an IT consulting company, I was assigned to this client project to:
- Redesign backend architecture for scalability
- Implement caching, queuing, and audit systems
- Set up CI/CD pipeline and cloud infrastructure
- Optimize frontend with lazy loading & virtualization
Transformation Results
Architecture Decisions
Each decision follows: Problem → Solution → Measurable Impact
Cursor-Based Pagination
LIMIT/OFFSET caused full table scans on 500K+ records, resulting in 3-5 second queries.
Implemented encoded cursor pagination with consistent O(1) query time.
Query time reduced from O(n) to O(1) regardless of page number.
Multi-Layer Caching Strategy
Every request hit database directly. 500 req/sec overwhelmed MySQL instance.
Redis-first cache with automatic in-memory fallback during outages.
10x reduction in database load, zero downtime during Redis failures.
Async Job Processing (BullMQ)
Email/PDF generation blocked API responses for 5-10 seconds.
Redis-backed job queue with graceful degradation pattern.
API response < 200ms regardless of background task load.
Comprehensive Audit Trail
No visibility into data changes. Compliance concerns for enterprise clients.
Dual-layer audit: automatic interceptor + manual service pattern with redaction.
SOC 2 / ISO 27001 compliance ready. Full change history with rollback.
Rate Limiting & Security
API vulnerable to DDoS, brute force attacks, and common web vulnerabilities.
Three-tier rate limiting (burst/medium/long) with Helmet.js security headers.
OWASP Top 10 compliance, brute force protection.
Frontend: Table Virtualization & Lazy Loading
Rendering 10K+ rows caused browser freeze. Large bundle size slowed initial load.
Virtual scrolling for tables, code-splitting, and lazy loading for heavy components.
Smooth 60fps scrolling on large datasets. 40% reduction in initial bundle size.
Technology Stack
Cloud Architecture
┌─────────────────┐
│ Route 53 │
│ (DNS) │
└────────┬────────┘
│
┌────────▼────────┐
│ ALB │
│ (HTTPS + WAF) │
└────────┬────────┘
│
┌────────────────────┼────────────────────┐
│ │ │
┌───────▼───────┐ ┌───────▼───────┐ ┌───────▼───────┐
│ NestJS API │ │ NestJS API │ │ NestJS API │
│ (EC2 + ASG) │ │ (EC2 + ASG) │ │ (EC2 + ASG) │
└───────┬───────┘ └───────┬───────┘ └───────┬───────┘
│ │ │
└───────────────────┼───────────────────┘
│
┌───────────────────┼───────────────────┐
│ │ │
┌───────▼───────┐ ┌───────▼───────┐ ┌───────▼───────┐
│ Aurora MySQL │ │ ElastiCache │ │ S3 │
│ (Multi-AZ) │ │ (Redis) │ │ (Assets) │
└───────────────┘ └───────────────┘ └───────────────┘Multi-AZ deployment with auto-scaling, Redis caching, and S3 for static assets.
CI/CD Pipeline
Automated pipeline: 30 min manual → 5 min automated
Key Takeaways
This project taught me that scalability isn't about rewriting everything — it's about identifying the right bottlenecks and solving them incrementally.
The most valuable skill? Knowing when to use boring, proven patterns over clever solutions that break at 2 AM.
What I'd Do Differently
- Start with observability before optimization
- Implement feature flags for safer deployments
- Set up load testing earlier in the process
- Document architectural decisions from day one