Near-real-time and event-driven data systems
Designing cloud data flows for operational systems where freshness, resilience and traceability mattered.
- Context
- Operational applications needed timely data movement between source systems, cloud services and downstream consumers.
- Constraint
- The integration had to tolerate partial failures, variable event volume and sensitive operational data.
- My role
- I led technical design and implementation across ingestion, processing, reliability controls and deployment.
- Architecture
- Event-driven ingestion, streaming and asynchronous processing, durable storage, API integration and monitored delivery pipelines.
- Implementation
- I designed processing boundaries, retry behaviour, deployment automation and operational checks for data movement and downstream availability.
- Result
- The resulting system supported timely operational data delivery with explicit failure handling and traceable processing stages.
- What I learned
- Near-real-time architecture is valuable only when replay, failure isolation and operational support are designed with the happy path.