data-flow-designer

Trace asynchronous and synchronous data paths by mapping the happy path first, then documenting failure and retry paths. This method clarifies data movement and ownership while highlighting where duplication, staleness, or race conditions can occur in event-driven workflows.

Defining data provenance requires explicitly marking trust boundaries, validation points, and persistence events across the architecture. This clarifies data ownership at each hop and ensures explicit handling of validation, persistence, and error paths throughout the system design.

Yes, this approach applies directly to event-driven and batch workflows where data ownership and trust boundaries must be explicit. It helps distinguish synchronous user-facing flow from asynchronous background flow while tracing transformations across source and sink systems.

Highlight duplication and staleness risks by tracing the happy path first, then mapping failure and retry paths to expose race conditions. Mapping data movement across frontend, backend, and storage layers makes these vulnerability points explicit in your architecture.

You need to map trust boundaries and validation points when features span multiple layers or asynchronous processes. Making these boundaries explicit ensures proper handling of data provenance, ownership, and error paths across complex event-driven or batch workflows.