CSV import interface

available from ID Validation 1.5.0 / free for all existing customers
We recommend using the XLSX or JSON import interfaces.

By using a simple CSV file, the module provides you with. “Duplicate Check” a way to check your entire dataset.

We take care to maintain compatibility when extending the CSV duplicate interface. This means that you can always use the latest version without generating additional effort when integrating it into your ERP system.

In order to guarantee the affiliation of the individual duplicate data records in your master data, you have the possibility, to specify up to two unique keys in the import file.

The default separator of the individual elements for the duplicate check is the ‘|’ character (pipe). This can be changed via the settings. Bold field names are mandatory fields (the separator can be changed via settings).

Please note that all fields must be specified in the import file, even if you do not use Key_1 and Key_2.

Structure - import file

fieldformatexample
key1String
key2String
firstnameString
lastnameString
name1String
name2String
name3String
name4String
streetString
numberString
postcodeString
townString
departmentString
countryString

Example in the form of a CSV file:

key1;key2;firstname;lastname;name1;name2;name3;name4;street;number;postcode;town;department;country;
val_key1;val_key2;val_firstname;val_lastname ;val_name1;val_name2;val_name3;val_name4;val_street;val_number;val_postcode;val_town;val_department;val_country;

… (more duplicate checks)

Note

When creating the CSV import file, please pay attention to the correct number of columns (14 columns). This note is important for possible errors during import when using CSV. However, you can also use the XLSX or JSON import format to eliminate this source of errors.

Structure - export file

The CSV export file of the duplicate check contains the transferred values as well as cleaned and marked as duplicate.

fieldformatexample
internalidString
key1String
key2String
firstnameString
lastnameString
name1String
name2String
name3String
name4String
streetString
numberString
postcodeString
townString
departmentString
countryString
// cleaned data
cleaned firstnameString
cleaned lastnameString
cleaned name1String
cleaned name2String
cleaned name3String
cleaned name4String
cleaned streetString
cleaned numberString
cleaned postcodeString
cleaned townString
cleaned departmentString
cleaned countryString
// applied cleaners
applied cleanersString
// applied duplicates
duplicate idsString
address groupString