Problem Statement
The client faced severe API slowness, causing delays in data processing, poor system performance, and user frustration. Faster API response times were urgently needed.
Challenges & Solutions
- Slow API Response → Optimized queries & caching reduced delays.
- Limited Test Data → Created real-world test datasets for better evaluation.
- High Database Load → Refactored database & added indexing for faster execution.
- Lack of Monitoring → Integrated AWS CloudWatch & OpenTelemetry for real-time tracking.
- Scalability Issues → Migrated to microservices with Kubernetes for better handling of high traffic.
Old vs. New System
| Aspect | Old System (Before) | New System (After) |
| API Speed | 30+ sec delays | Fast response time |
| Testing | Limited real-world data | Accurate performance testing |
| Monitoring | No real-time tracking | Instant issue detection |
| Scalability | Struggled with high load | Auto-scaling microservices |
| Deployment | Manual & error-prone | Automated CI/CD pipeline |
| User Experience | Slow & frustrating | Fast & smooth interactions |
Technologies Used
- AWS CloudWatch, OpenTelemetry – Real-time monitoring
- PostgreSQL, Redis – Faster database performance
- Kubernetes, Docker – Scalable infrastructure
- Jira, Postman – Agile project tracking & testing
Results & Impact
✅ 30x Faster API Speed – Response time improved drastically
✅ Better System Reliability – Real-time monitoring reduced downtime
✅ Scalable & Future-Ready – Microservices ensure smooth growth
Conclusion
With these upgrades, the client now has a faster, more stable, and scalable system, ensuring a better user experience and long-term efficiency.
