A Tier-1 International Bank

This enterprise-grade solution handles real-time financial data processing with sub-second latency, designed to support regulatory and security requirements while targeting high availability.

Challenge

A major international banking group needed to migrate from a legacy ETL-based data processing model to a real-time streaming infrastructure. The existing ETL system was too slow, too rigid, and not designed for real-time processing. The new platform had to:

• Process transactions and events in real time
• Enable near-real-time fraud signal processing
• Continuously update risk models
• Handle massive data volumes stably
• Scale seamlessly – without downtime
• Fully meet compliance and security requirements

In short: a real-time streaming architecture that combines banking standards with startup speed.

Our Approach

Event-Driven Architecture

We developed a fully event-driven backend platform based on Apache Kafka as the central messaging backbone.

Microservices + Kubernetes

All services were containerized and orchestrated in a Kubernetes cluster:

• automatic scaling
• self-healing
• rolling deployments without downtime

Data Integrity at Massive Throughput

To prevent data loss and duplicates with millions of events, we implemented:

• a custom retry engine
• deduplicated event processing
• robust commit strategies
• in-memory caching for hot paths

Reliability & Observability

Monitoring, logging, and alerting are based on:

• Prometheus
• Grafana
• ELK Stack

This gives the client team real-time insight into latencies, throughput, and system health.

Results

• Multi-million messages per second throughput
• Sub-second latency under peak load
• Designed for high availability and resilience
• Significant reduction in operational overhead through simplified data pipeline

Technical Stack

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.