The current method of handling large data inputs and outputs in data pipelines using JSON for small automations and a symlinked shared folder for large datasets is unscalable and limits distributed processing.

Workarounds Described

• using a folder that we symlink to be shared by all steps if a specific flag for that flow is set
• forcing us to have the same worker process all the steps inside that flow

Implied Software Gaps

• A distributed data sharing mechanism that allows different worker processes to access shared data efficiently.
• A workflow orchestration system that can distribute steps across multiple workers while maintaining data consistency.

DataFlow Nexus

DataFlow Nexus provides a robust, scalable solution for managing large data inputs and outputs in data pipelines. It enables distributed processing without relying on local filesystem symlinks, improving performance and scalability for data-intensive workflows.

Existing Solutions Mentioned