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How To Fix RSDMD333 - Characteristic &: & percent of the & data records deleted from table &


SAP Error Message - Details

  • Message type: E = Error

  • Message class: RSDMD - Messages for master data

  • Message number: 333

  • Message text: Characteristic &: & percent of the & data records deleted from table &

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  • 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?

    First, use our AnswerBot below to get a possible cause and solution (requires a premium subscription).

    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.


Smart SAP Assistant

  • What is the cause and solution for SAP error message RSDMD333 - Characteristic &: & percent of the & data records deleted from table & ?

    The SAP error message RSDMD333 indicates that a certain percentage of data records have been deleted from a specific table related to a characteristic in the SAP BW (Business Warehouse) system. This message typically arises during data loading or data processing activities, particularly when working with InfoObjects or InfoProviders.

    Cause:

    1. Data Quality Issues: The records being deleted may not meet the criteria for the characteristic, such as missing mandatory fields or invalid data.
    2. Data Cleansing: During the data load process, the system may have identified records that do not conform to the defined data model or business rules, leading to their deletion.
    3. Transformation Rules: If there are transformation rules that filter out certain records based on specific conditions, this could lead to a significant percentage of records being deleted.
    4. Data Source Changes: Changes in the data source or the structure of the data being loaded can also lead to discrepancies, resulting in record deletions.

    Solution:

    1. Check Data Source: Review the data source to ensure that it is providing the expected data. Validate that the data structure and content align with the requirements of the InfoObject.
    2. Review Transformation Rules: Examine the transformation rules applied during the data load process. Ensure that they are correctly configured and not overly restrictive.
    3. Data Quality Checks: Implement data quality checks to identify and rectify issues in the source data before loading it into SAP BW.
    4. Monitor Load Process: Use the SAP BW monitoring tools to track the data load process and identify at which stage the records are being deleted.
    5. Adjust InfoObject Settings: If necessary, adjust the settings of the InfoObject to accommodate the incoming data better, ensuring that it aligns with the expected data model.
    6. Consult Logs: Check the application logs and error logs for more detailed information about which records were deleted and why.

    Related Information:

    • Transaction Codes: Use transaction codes like RSA1 (Data Warehousing Workbench) to monitor and manage data loads.
    • Data Load Monitoring: Utilize the Data Load Monitor to analyze the status of data loads and identify any issues.
    • SAP Notes: Search for relevant SAP Notes that may address specific issues related to the RSDMD333 message or provide guidance on best practices for data loading in BW.
    • Documentation: Refer to SAP BW documentation for best practices on data modeling, data loading, and transformation processes.

    By following these steps, you should be able to identify the root cause of the RSDMD333 error message and implement appropriate solutions to mitigate the issue.

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