Capital Markets Data Platform: Unifying Real-Time Feeds

The Challenge

A capital markets firm needed to consolidate data from 12 trading platforms, 3 clearinghouses, and multiple market data feeds into a unified platform. The existing system relied on batch processing with overnight ETL jobs, meaning traders operated on stale data for critical risk calculations.

Regulatory requirements (MiFID II, SOX) demanded complete data lineage tracking and the ability to reconstruct the state of any position at any point in time.

Our Approach

We architected a streaming-first data platform using Apache Kafka as the backbone, with .NET microservices for domain-specific processing and PostgreSQL for persistent storage. The key design decisions:

Event Sourcing: Every state change was captured as an immutable event, providing natural audit trails and point-in-time reconstruction. This eliminated the need for separate audit logging infrastructure.

CQRS Pattern: Read models were optimized separately from write models. Trading dashboards queried pre-computed projections updated in real-time, while the source-of-truth event store maintained full fidelity.

Data Lineage: Every derived value carried metadata tracing it back to its source: which market data feed, which timestamp, which calculation version. This satisfied regulatory requirements for explainability.

The frontend was built with React and real-time WebSocket connections, providing traders with sub-second updates to risk calculations, position summaries, and P&L figures.

Results

• 2M+ daily transactions processed with sub-second latency
• Point-in-time reconstruction available for any position within 3 seconds
• Complete data lineage from source feed to dashboard metric
• Regulatory audit passed with zero findings on data governance

The platform replaced a legacy system that had accumulated 8 years of technical debt, providing a modern foundation for the firm's next decade of growth.