Case Study
CQRS and CDC-Based Read Optimization for Digital Banking
Designed a CQRS-based architecture to separate read and write workloads, improve dashboard responsiveness, and reduce dependency on core transactional systems using Oracle GoldenGate and a read-optimized model.
Solution Architecture Diagram
High-level view of the CQRS architecture separating transactional write flows from read-optimized customer-facing query paths using CDC-based replication.
The write path preserves transactional integrity, while the read path uses CDC-based replication to serve faster queries.
Overview
Designed a CQRS-based architecture for digital banking workloads to separate read and write concerns, improve dashboard responsiveness, and reduce dependency on core transactional systems. The solution used Oracle GoldenGate for change data capture and a read-optimized store to support faster customer-facing experiences while preserving the integrity of the write path.
Business Context
Digital banking channels required fast and scalable access to customer data for dashboards, account summaries, and real-time experiences. Traditional synchronous integration with core systems introduced latency and scalability limitations, especially for read-heavy use cases.
Challenge
The platform was handling both read and write workloads through tightly coupled enterprise paths. This resulted in high latency for read-heavy journeys, unnecessary load on core systems, limited scalability for customer-facing APIs, and dependency on upstream system performance.
Architecture Approach
A CQRS-based model was introduced to separate write operations from read operations. Writes continued through the enterprise transaction path, while data changes were captured using Oracle GoldenGate and replicated into a read-optimized store. Customer-facing APIs consumed this read model for faster and scalable data access.
Key Architecture Decisions
Separated read and write responsibilities instead of optimizing a single path. Chose CDC-based replication over synchronous duplication to reduce coupling. Accepted eventual consistency for suitable use cases. Preserved the transactional system of record as the source of truth. Optimized the read model specifically for channel consumption patterns.
Trade-offs and Considerations
Introduced eventual consistency between write and read models. Added dependency on replication health and latency. Required careful identification of use cases where near-real-time consistency was acceptable. Increased operational complexity in maintaining the read model.
Outcome
Improved responsiveness for read-heavy journeys, reduced load on transactional systems, and enabled scalable digital channel experiences. Established a foundation for future read optimization and independent evolution of customer-facing APIs.
Key Takeaways
Read/Write Separation
Treated read and write workloads differently for efficiency.
CDC-Based Replication
GoldenGate enabled low-coupling data replication.
Performance Optimization
Designed for fast customer-facing queries.