How To Fix RSCNV900 -

RSCNV900 - Overview

• Message type: E = Error

• Message class: RSCNV - Messages class for Metadata Remodeling Toolbox

• Message number: 900

• Message text:

Self-Explanatory Message

Since SAP believes that this specific error message is 'self-explanatory,' no more information has been given.The majority of messages in the SAP system have a message text, however this is frequently insufficient to comprehend or resolve the problem.

To make things easier, more detailed information is frequently added to describe the issue, how to fix it, and the necessary steps or configuration modifications.

Unfortunately, there isn't any extra information in this error notice.

What else can you do?

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Also, review the in-depth Common Questions & Answers listed below; you could discover a solution there or be able to connect with others who have faced similar challenges.

You can also try searching the SAP support portal (support.sap.com) but you need a special user ID to access it. It is possible that an SAP support note exists that provides additional details about the mistake or even steps for fixing it.

RSCNV900 - Details

• The SAP error message RSCNV900 typically relates to issues encountered during data conversion processes, particularly when using the SAP Data Transfer Workbench or during data migration activities. This error can occur for various reasons, including data inconsistencies, incorrect data formats, or issues with the configuration of the data transfer.
  
  Cause:
  
  Data Format Issues: The data being imported may not match the expected format or structure defined in the target system.
  Inconsistent Data: There may be inconsistencies in the data, such as missing mandatory fields or invalid values.
  Configuration Errors: Incorrect settings in the data transfer configuration can lead to this error.
  Mapping Issues: Problems with the mapping of source data to target fields can also trigger this error.
  
  
  Solution:
  
  Check Data Format: Ensure that the data being imported adheres to the required format. Validate the structure and types of data fields.
  Data Validation: Review the data for any inconsistencies or missing values. Correct any issues found before attempting