The challenge
A prominent automotive marketplace in Dubai was facing significant backend performance bottlenecks as their user base rapidly expanded. The platform connects buyers and sellers of vehicles, parts, and services but struggled with slow API responses and frequent outages.
The client engaged Code Elevator to overhaul their backend system to enhance performance, reliability, and prepare for future growth.
- Existing backend architecture was monolithic, causing slow response times and difficult scalability
- API endpoints were frequently timing out during peak traffic hours
- Data synchronization issues between different microservices led to inconsistent user data
- The system lacked robust monitoring and automated alerting for failures
- Needed seamless integration with multiple third-party automotive data providers
Solutions
Code Elevator assembled a dedicated backend development team specializing in scalable, high-performance architectures:
- 3 Backend Developers (Node.js, Java)
- 1 DevOps Engineer
- 1 QA Engineer
Execution
Phase 1: System Audit & Redesign (2 Weeks)
Conducted thorough code and infrastructure audit to identify bottlenecks
Designed new microservices architecture using Node.js and Java Spring Boot
Planned data synchronization strategies with event-driven architecture using KafkaPhase 2: Backend Development & Refactoring (5 Weeks)
Refactored legacy monolith into independent microservices
Implemented fast, RESTful APIs with caching layers via Redis
Developed data sync pipelines and message queues for real-time consistency
Integrated external automotive data APIs with error handling and retriesPhase 3: Monitoring, Testing & Deployment (2 Weeks)
Deployed Prometheus and Grafana for monitoring system health and performance
Automated testing suite using Mocha and JUnit for stability assurance
Configured Kubernetes for container orchestration with auto-scaling.
Code Elevator’s backend overhaul transformed our platform. We now have a resilient system that handles growing traffic with ease, which was critical for our business expansion.
CTO, Dubai Automotive Marketplace
Results
- API Response Time: Reduced average API latency by 65%
- System Uptime: Achieved 99.98% uptime, minimizing downtime incidents
- Data Accuracy: Real-time synchronization eliminated user data mismatches
- Scalability: Platform now scales automatically to handle traffic spikes smoothly
- Client Feedback: Praised for technical expertise and transparent communication
Key Outcomes
Tools & Technologies Used
- Backend: Node.js, Java Spring Boot, Kafka, Redis
- DevOps: Kubernetes, Docker, AWS EKS
- Monitoring: Prometheus, Grafana
- Testing: Mocha, JUnit
- APIs: RESTful integration with automotive data providers