Validate files
Map output files to a template to ensure columns and data formatting are accurate.
Validate files checks CSV lists against a OneSchema template so you can catch missing columns, incorrect data types, and formatting problems before the data moves further down your Multi FileFeed (MFF). It's the standard way to enforce a schema on files that have already been transformed, split, or extracted earlier in the feed.
Availability: GA
What it does
- Input: one or more CSV lists.
- Output: the same CSV lists, validated against a template. Rows or cells that fail validation are flagged so downstream nodes or your review team can act on them.
- The transform maps each file's columns to a OneSchema template, checking column presence, data types, and formatting rules (such as date formats or allowed values) defined on that template.
When to use it
- You need a final checkpoint that confirms files match an expected schema before they're delivered to a destination.
- Files come from vendors or partners whose formatting drifts over time (renamed columns, inconsistent date formats, etc.).
- You want a single, template-driven contract that multiple upstream sources must satisfy, regardless of how each source's file was originally structured.
- You're consolidating output from several branches of a Multi FileFeed and want to guarantee they all conform to the same shape before merging or delivering.
How to configure it
In the MFF builder, add a Validate files node after the node that produces the CSV lists you want to check.
Settings
| Setting | Description | Default |
|---|---|---|
| Template | The OneSchema template to validate files against. Column names, data types, required fields, and formatting rules are all drawn from this template. | None selected |
| Column mapping | How each file's columns line up with the template's columns. By default, columns are matched automatically by header name; you can adjust the mapping manually if headers don't match exactly. | Auto-mapped by header name |
Example
Input
A CSV list of customer orders with columns Order ID, Customer Email, Order Date, Amount.
Output
The same list, validated against an "Orders" template. Rows with an invalid Order Date format or a missing Customer Email are flagged, while conforming rows pass through unchanged.
Troubleshooting
Columns fail to map automatically
- Check that your file's header names closely match the template's column names.
- Adjust the column mapping manually in the settings panel if headers use different naming conventions than the template.
Valid-looking data is flagged as invalid
- Confirm the template's data type and formatting rules match what you expect (for example, date format or number precision).
- If the source data legitimately varies in format, consider adding a formatting transform upstream to normalize it before validation.
Too many rows are failing validation
- Review the template's required fields; a recently added required column can cause a spike in failures for files that predate it.
- Use Custom validation for rules that a standard template can't express, such as cross-column logic.
See also
Updated about 7 hours ago