Designing event-driven orchestration for scalable data workflows
As data workflows grew in complexity, traditional schedule-based orchestration became difficult to reason about and operate.
Context
As data workflows grew in complexity, traditional schedule-based orchestration became difficult to reason about and operate.
Problem
- Tight coupling between pipelines.
- Limited visibility into execution states.
- Poor handling of partial failures and retries.
Approach
- Introduced an event-driven orchestration model using managed cloud services.
- Decoupled pipeline stages through explicit events and state transitions.
- Improved observability and error handling across workflows.
Key decisions
- Used event-driven patterns instead of purely time-based scheduling.
- Designed workflows around idempotency and explicit state management.
- Centralized monitoring and alerting for all pipeline executions.
Result
Workflows became easier to reason about, more resilient to failure, and simpler to extend as new use cases emerged.
What I learned
- Event-driven orchestration scales better for complex platforms.
- Observability is essential when control flow becomes decentralized.