This enterprise-grade solution handles real-time financial data processing with sub-second latency, ensuring compliance with banking regulations while maintaining 99.99% uptime.
Challenge
The bank needed to migrate from a legacy ETL-based data processing model to a real-time streaming infrastructure that would support instant transaction monitoring, fraud detection, and risk analysis.
Our Approach
We implemented an event-driven architecture using Apache Kafka as the backbone for all asynchronous communication. Each service was containerized and orchestrated via Kubernetes, ensuring automatic scaling and fault tolerance. To maintain data integrity under high throughput, we built a custom retry and deduplication layer using PostgreSQL and in-memory caching.
Results
- Throughput: over 3 million messages/second
- Latency reduced from 5s to under 200ms
- 99.99% uptime achieved through redundancy
- Simplified data pipeline reduced operational overhead by 40%
Technical Stack
Backend: Java 17 · Spring
Streaming: Apache Kafka
Database: PostgreSQL
Infrastructure: Docker · Kubernetes
Duration: 9 months
Team: 5 engineers
Why it Matters
The same streaming-first principles and microservice orchestration logic are now core to H-Studio's backend designs for modern startups — where live analytics, event logs, and real-time customer data are essential.