General requirements
- Use stable headers or field names.
- Keep identifiers as strings when leading zeros or formatting matter.
- Use consistent date formats within a file.
- Avoid mixing summary rows with detail rows.
- Keep one logical record per row when using CSV-like formats.
CSV guidance
Use UTF-8 encoding and a consistent delimiter. If values can contain commas, quote them consistently. The header row names the fields. Darpan can build a flat schema directly from it — see Create a schema.Blank cells
A blank CSV cell is read as an empty value, not as a missing field. The distinction matters when a run compares the two sides:
A CSV cell holds text or holds nothing; the format has no way to express a null. Reporting a blank cell as an empty value keeps “this column was blank” distinguishable from “this column was not in the file”, which would otherwise produce a difference whose one side simply looked absent.
The compare path and the schema-inference header parser read CSV with the same options, so a file that parses one way in the wizard parses the same way in a run.
JSON guidance
Use predictable object keys and arrays. Avoid changing object nesting between runs unless the schema is updated with the change.Troubleshooting
If upload or parse behavior is unexpected, check encoding, delimiters, header names, blank lines, duplicate columns, and field type assumptions first.To define how source file fields map into Darpan, see Schema model.