Message type: E = Error
Message class: MDC_LOAD - Load Messages
Message number: 125
Message text: Cannot derive attribute information from column &1 in "&2"
At least one of the components you want to upload contains a reference
to an invalid data object. A data object is a part of the data model as
maintained in transaction MDCMODEL, and only names of objects that are
marked as "process relevant" are considered valid during imports of
files.
For the import to work, it is essential that every column in your file
corresponds to a valid data object and a valid field of that data
object. If one of them is invalid (or empty), the import will fail and
raise this or a similar error message.
Make sure all names of all components of your file (i.e. workbook sheets
when your file is a Microsoft Excel document, or CSV file names when
your file is either a CSV file or a CSV file package) start with a valid
data object name. Run transactions MDCMODEL or MDCIN to compare the
names.
If the table name is empty or the column mentioned in this message seems
to be empty when you check your file, there might be invisible
characters in your document. For example, this can occur when cells of a
workbook sheet are not entirely deleted. In this case, select the
complete column in your document by clicking its header<(>,<)>
right-click the selected column(s), and choose "Delete" in the context
menu.
The system issues an error message and will not allow you to continue with this transaction until the error is resolved.
Error message extract from SAP system. Copyright SAP SE.
MDC_LOAD125
- Cannot derive attribute information from column &1 in "&2" ?The SAP error message MDC_LOAD125 indicates that the system is unable to derive attribute information from a specific column in a given table or data source. This error typically occurs during the data loading process in SAP HANA or when working with SAP Data Warehouse solutions.
Cause: The error can be caused by several factors, including: Data Type Mismatch: The column in question may have a data type that is not compatible with the expected attribute type in the data model. Missing Metadata: The metadata for the column may be incomplete or missing, preventing the system from understanding how to interpret the data. Incorrect Configuration: The configuration settings for the data model or the data source may not be set up correctly, leading to issues in data extraction or transformation. Column Name Issues: The column name may not match the expected naming conventions or may contain special characters that are not handled properly. Data Source Issues: The
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MDC_LOAD124
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