Data Engineering Financial Services
Data Lakehouse Architecture for a Major Financial Services Firm
How we designed and implemented a unified data lakehouse that reduced data processing time by 70% and enabled real-time analytics for risk management.
Client: Fortune 500 Financial Services
The Challenge
A major financial services firm was struggling with data silos across multiple legacy systems. Their risk management team needed real-time access to consolidated data, but their existing batch-processing architecture introduced delays of up to 24 hours.
Our Approach
We designed a modern data lakehouse architecture using Delta Lake on AWS, combining the flexibility of a data lake with the reliability of a data warehouse.
Key Components
- Streaming Ingestion: Apache Kafka for real-time data capture from 50+ source systems
- Delta Lake: ACID-compliant storage layer with time travel and schema evolution
- Medallion Architecture: Bronze, Silver, and Gold layers for progressive data refinement
- Real-time Serving: Low-latency query layer using Databricks SQL
Results
- 70% reduction in data processing time
- Real-time risk dashboards replacing 24-hour-old reports
- $2M annual savings in infrastructure costs through consolidation
- Unified governance across all data assets
Key Takeaways
- Start with clear business outcomes, not technology choices
- Invest in data quality from day one
- Build for evolution — the lakehouse pattern allows incremental migration
- Empower business users with self-service analytics