Files can be uploaded via the Editor interface - first choose the library and table, then click "Upload". All versions of excel are supported. If loading very large files (eg over 10mb) it is more efficient to use CSV format, as this bypasses the local rendering engine, but also the local DQ checks - so be careful! For CSV, alternative delimiters can be used (eg semicolons).
Uploaded data may *optionally* contain a column named `_____DELETE__THIS__RECORD_____` - where this contains the value "Yes" the row is marked for deletion.
Thanks to our pro license of [sheetJS](https://sheetjs.com/), we can support all versions of excel, large workbooks, and extract data extremely fast. We also support the ingest of [password-protected workbooks](/videos#Uploading-a-password-protected-excel-file).
* data can contain additional columns (they are just ignored)
* data can be completely surrounded by other data
A copy of the original Excel file is also uploaded to the staging area. This means that a complete audit trail can be captured, right back to the original source data.
- Variable names must match the target (not case sensitive). An easy way to ensure this is to download the data from Viewer and use this as a template.
- The order of variables does not matter EXCEPT for the (optional) `_____DELETE__THIS__RECORD_____` variable. When using this variable, it must be the **first**.
This means that uploaded date / datetime values should be unambiguous (eg `01FEB1942` vs `01/02/42`), to avoid confusion - as the latter could be interpreted as `02JAN2042` depending on your locale and options `YEARCUTOFF` settings. Note that UTC dates with offset values (eg `2018-12-26T09:19:25.123+0100`) are not currently supported. If this is a feature you would like to see, contact us.