walkeros-understanding-flow

The walkerOS data pipeline architecture follows a Source → Collector → Destination pattern. Sources feed event data into the collector, transformers enable modular processing, and destinations deliver results in a composable workflow.

Transformers enable modular processing by sitting within the collector stage of the data pipeline. They allow individual event data points to be modified or enriched before being delivered to destinations, ensuring separation of concerns.

To design an event pipeline, map a simple source-to-destination flow by defining how sources feed the collector, applying transformers for modular processing, and configuring destinations to deliver the final results.

No external tooling is required to work with walkerOS event pipelines. The architecture relies on standard TypeScript and JSON references, requiring no additional dependencies beyond the core walkerOS framework.

walkerOS emphasizes separation of concerns in data pipelines to ensure components communicate cleanly. The Source, Collector, and Destination stages operate independently, making it easier to design, validate, and evolve event workflows.

Yes, you can use standard TypeScript and JSON to validate a walkerOS data flow. The pipeline architecture is designed to work natively with these formats, allowing you to verify event structures without external tooling.