JSON and CSV data exports

An export is an interface for its next consumer. It should preserve the meaning of the data and explain how to read it. Format choice, field names, encoding, and the handling of missing values matter as much as producing a downloadable file.

Keep the meaning intact

Choose JSON for structures with nested relationships when the receiving system expects them. Choose a defined tabular format when rows and columns are the useful model. In either case, document units, identifiers, and time conventions, then test the export with the actual receiving application.

A practical sequence

• Identify the consumer and its accepted file format.
• Define a schema with clear types and stable field names.
• Specify encoding, missing-value rules, and date conventions.
• Validate the finished file and include useful provenance.

Watch for this failure mode

A file can open successfully while corrupting identifiers, changing date interpretation, or dropping precision. Use representative round-trip examples, including leading zeroes, non-English text, and large numbers.

Make the handoff clear

Keep export versioning visible when field meanings change. A new optional field and a renamed required field have different compatibility consequences. Document how a consumer can detect the version and what should happen when it encounters unfamiliar data.

Before expanding the workflow, write one representative success case and one useful failure case. Agree on the result shape and how a reviewer should decide whether the next step can proceed. Keep the example small enough that the assumptions remain visible.

Go deeper

Read the complete exports guide for examples and design decisions. Connect this step to parsing, or return to the workflow library to map the whole sequence.