Client Context
A financial services company replacing overnight batch reporting relied on legacy batch jobs, cloud streams, lakehouse, APIs, and reporting tools to support positions, balances, risk calculations, and operational alerts. The operating model handled high-volume daily and intraday processing across global operations, using transaction, position, reference, and risk data. Real-time services had to match established batch results and control totals. Timing and sequence differences created difficult reconciliation defects.
Challenges
- Real-time services had to match established batch results and control totals.
- Timing and sequence differences created difficult reconciliation defects.
- The legacy legacy batch jobs, cloud streams, lakehouse, APIs, and reporting tools architecture slowed changes to positions, balances, risk calculations, and operational alerts and created tightly coupled release risk.
- Limited automated coverage made it difficult to compare legacy and modernized behavior.
- Database, API, and session dependencies created hidden failure points during incremental refactoring.
Solutions Implemented
- Built batch-versus-stream reconciliation, contract, sequence, and latency validation.
- Introduced shadow processing and controlled switch-over by report domain.
- Mapped critical dependencies and established behavior, data, and performance baselines before refactoring.
- Created contract, component, API, and end-to-end tests for modernized positions, balances, risk calculations, and operational alerts.
- Validated container, microservice, gateway, and database changes through automated pipeline gates.
- Executed parallel runs, traffic simulation, observability checks, and phased cutover validation.
Value Delivered
- Reduced the primary testing or operational effort by approximately [45%], subject to validation against approved engagement data.
- Protected metric accuracy during architectural change.
- Reduced regression risk during incremental modernization.
- Created automation assets aligned to the new cloud-native operating model.
Impact Highlights
- Achieved an estimated [35%] improvement in cycle time, coverage, or processing consistency; replace with the approved client metric.
- Reduced relevant defects, failures, or rework by an illustrative [20%]; confirm before publication.
- Faster insight delivery without losing historical control confidence.
- Enabled evidence-based retirement of legacy components.
