Which scenario would benefit from using a fanout?

• A. You want to create a separate CSV file for each layer in your source dataset.
• B. You want the workspace to work with a dynamic schema, outputting whatever data it is given.
• C. You want to let the user choose the format for their output.
• D. You want to store a local version of all ports in your workspace for faster development.

Answer: (A)

Fanout duplicates one input stream into multiple downstream paths, letting you branch the data so different subsets can be written to different outputs. This is exactly what you need when you want a separate CSV file for each layer: you can route features belonging to each layer to its own writer, producing distinct files in parallel from the same source data.

In practice, you’d send the single dataset into the fanout, then use filters or testers to separate features by layer on each branch, with each branch connected to its own CSV writer. This makes managing per-layer outputs straightforward without creating multiple full copies of the workspace logic.

The other scenarios don’t hinge on creating multiple parallel outputs from one input. Dynamic schema handling, user-selected output formats, or caching ports for development goals are addressed by other tools or workflow patterns, not by fanout.