How We Built a Real-Time Financial Dashboard Processing 50K+ Transactions Per Second

# How We Built a Real-Time Financial Dashboard Processing 50K+ Transactions Per Second ## Overview FinEdge Capital, a rapidly growing fintech unicorn processing over $2 billion in monthly transactions, was facing a critical infrastructure bottleneck. Their legacy analytics system, built on traditional database architecture, was buckling under the weight of exponential growth. Decision-makers were waiting up to 12 seconds for critical dashboard queries—unacceptable in a market where milliseconds translate to millions in opportunity cost. Webskyne was engaged to architect and deliver a next-generation real-time financial analytics platform that could not only handle their current volume but scale to support 10x growth over the next 24 months. The project spanned infrastructure redesign, frontend modernization, and the implementation of streaming analytics pipelines capable of sub-second query responses. ## Challenge FinEdge's existing analytics infrastructure presented multiple critical challenges that threatened their operational velocity: **Latency Crisis**: Their PostgreSQL-based analytical queries took 8-12 seconds to execute during peak hours. This delay meant trading teams were making decisions on data that was already stale—equivalent to trading with outdated market information. **Scalability Ceiling**: The monolithic architecture could not horizontally scale. During Q4 2025, Black Friday-level traffic caused system crashes that cost an estimated $2.1 million in lost transaction opportunities over a single weekend. **Data Silos**: Customer transaction data, behavioral analytics, and fraud detection systems operated on separate databases with no real-time synchronization. Marketing teams couldn't correlate campaign performance with actual conversion data. **Developer Friction**: The existing React codebase was a tangle of legacy code with 18 months of technical debt. Adding new metrics or dashboards required 2-3 weeks of development cycle, slowing their ability to respond to market opportunities. **Infrastructure Costs**: Monthly database costs had grown 340% over 18 months, scaling linearly with transaction volume rather than optimally. ## Goals We established clear, measurable objectives aligned with FinEdge's business priorities: 1. **Reduce Query Latency**: Achieve sub-200ms query response for 95th percentile of dashboard requests 2. **Scale Infrastructure**: Support 10x current transaction volume without architectural changes 3. **Unify Data Pipeline**: Create single source of truth with real-time synchronization across all systems 4. **Accelerate Development**: Reduce new dashboard implementation time from weeks to days 5. **Optimize Costs**: Reduce infrastructure costs by 40% while improving performance ## Approach Our approach combined modern distributed systems architecture with pragmatic business alignment: ### Phase 1: Discovery and Architecture Design (Weeks 1-3) We began with intensive stakeholder interviews across all departments—trading, risk, marketing, and engineering. This discovery revealed that the "real-time" requirement varied significantly by use case: trading desks needed sub-second updates, while executive dashboards could tolerate 5-minute refresh windows. Our architecture leveraged this insight to implement a tiered caching strategy rather than a one-size-fits-all solution. We designed a stream-based architecture using Apache Kafka for event streaming, with differentiated query paths for real-time vs. analytical workloads. ### Phase 2: Infrastructure Modernization (Weeks 4-8) We migrated the analytical workload from PostgreSQL to ClickHouse, a column-oriented database optimized for analytical queries. This change alone reduced baseline query times from 12 seconds to 1.2 seconds. We implemented Redis clusters for hot data caching, with a custom invalidation strategy that maintained cache coherence while minimizing stale data risk. The Kafka streaming pipeline now processed 50,000+ events per second with end-to-end latency under 50ms. ### Phase 3: Frontend Transformation (Weeks 9-14) The dashboard frontend was completely reimagined using React with TypeScript and a component-first design system. We built reusable chart components, metric cards, and filter systems that reduced new dashboard implementation from weeks to days. WebSocket connections replaced long-polling, enabling true real-time updates to trading dashboards. The new component architecture allowed FinEdge's internal team to build new visualizations without touching the core platform code. ### Phase 4: Optimization and Handoff (Weeks 15-17) The final phase focused on performance tuning, load testing, and knowledge transfer. We conducted rigorous chaos engineering tests, simulating various failure scenarios to ensure system resilience. ## Implementation ### Technical Architecture The implemented architecture comprised several interconnected systems: **Event Ingestion Layer**: Apache Kafka clusters formed the backbone, processing events from multiple source systems. Custom Kafka Connectors enabled seamless integration with FinEdge's transaction databases, fraud detection systems, and third-party data feeds. **Stream Processing**: Apache Flink handled event-time processing and windowed aggregations. We implemented custom watermarking strategies to handle out-of-order events while maintaining accurate window calculations. **Storage Tiers**: - **ClickHouse**: Primary analytical store with monthly partitions and custom sort orders optimized for common query patterns - **Redis Cluster**: Hot cache with 5-minute TTL for frequently accessed metrics - **Apache Iceberg**: Historical data lake for long-term storage and ad-hoc queries **API Gateway**: Custom GraphQL gateway with query batching and persistent subscriptions for real-time updates. Response caching at the gateway level reducedbackend load by 60%. ### Frontend Implementation The React dashboard was rebuilt with: - TypeScript for type safety across the entire codebase - Custom hooks for WebSocket management and reconnection logic - Canvas-based charting library for handling 10,000+ data points without performance degradation - Component library with 47 reusable components documented in Storybook - Automated testing with 89% code coverage ### Data Pipeline A typical data flow example illustrates the system's capability: 1. Transaction event triggers at payment gateway (12:00:00.001) 2. Kafka producer sends to `transactions` topic (12:00:00.005) 3. Flink processes and updates rolling aggregates (12:00:00.050) 4. ClickHouse receives pre-computed metrics (12:00:00.120) 5. Redis cache invalidated and refreshed (12:00:00.135) 6. Dashboard WebSocket pushes update to client (12:00:00.145) Total end-to-end latency: 144ms—98.8% improvement from the original 12-second query time. ## Results The platform launched in March 2026 and exceeded all performance targets: **Query Performance**: - Average dashboard query time: 147ms (target: <200ms) - 99th percentile: 312ms (target: <500ms) - Maximum observed: 890ms during 5x traffic spike **Scalability**: - Successfully processed 52,000 transactions/second during peak testing - Linear scaling achieved up to 100,000 events/second - Zero downtime during Black Friday 2026 traffic (3x normal volume) **Development Velocity**: - New dashboard implementation: 3 days average (down from 2-3 weeks) - Feature deployment frequency: weekly (up from monthly) - Bug resolution time: 4 hours average (down from 2 days) **Cost Optimization**: - Infrastructure costs reduced by 47% despite 3x traffic increase - Compute costs optimized through spot instance usage for non-critical workloads - Cache hit rate of 94% dramatically reduced database query costs ## Metrics | Metric | Before | After | Improvement | |--------|--------|--------|--------------| | Average Query Latency | 12,000ms | 147ms | 98.8% | | Peak Transaction Volume | 15,000/sec | 52,000/sec | 246% | | Dashboard Load Time | 8.2 seconds | 1.1 seconds | 86.6% | | Monthly Infrastructure Cost | $124,000 | $65,700 | 47% | | Deployment Frequency | Monthly | Weekly | 400% | | New Feature Time-to-Market | 18 days | 3 days | 83% | | System Uptime | 99.2% | 99.97% | 0.77% | ## Lessons ### 1. Tiered Caching is Essential Not all data requires the same freshness. Implementing differentiated caching strategies based on use case criticality delivered 10x the performance at a fraction of the cost. Trading desks need sub-second updates; executive dashboards can wait. ### 2. Invest in Observability Upfront We implemented comprehensive tracing, logging, and metrics collection from day one. When production issues arose, mean time to resolution was under 30 minutes—compared to industry averages of 4-6 hours. The investment paid dividends within the first month. ### 3. Component-First Architecture Pays Off Building reusable frontend components from the start seemed to slow initial development. However, by project completion, 73% of new dashboards were composed of existing components, delivering 4x development velocity. ### 4. Data Migration Requires Conservative Rollout We initially planned a big-bang migration but wisely chose gradual Cutover. This decision exposed three critical data synchronization issues that would have caused significant post-launch problems. ### 5. Performance Testing Must Be Continuous Load testing in staging revealed 85% of performance bottlenecks. However, 15% of issues only appeared under production traffic patterns. We now run continuous performance testing in production parallel environments. --- **Project Duration**: 17 weeks **Team Size**: 6 engineers (2 backend, 2 frontend, 1 devops, 1 technical lead) **Technology Stack**: Apache Kafka, ClickHouse, Redis, React, TypeScript, Apache Flink, GraphQL **Client**: FinEdge Capital **Sector**: Fintech --- *This case study demonstrates Webskyne's expertise in building high-performance financial analytics platforms. Contact us to discuss how we can transform your data infrastructure.*