How To Fix SAC_TOOLS102 - Internal error in APC kernel

SAP Error Message - Details

• Message type: E = Error

• Message class: SAC_TOOLS - Message Class for ABAP Channels tools

• Message number: 102

• Message text: Internal error in APC kernel

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.

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Smart SAP Assistant

• What is the cause and solution for SAP error message SAC_TOOLS102 - Internal error in APC kernel ?

  The error message "SAC_TOOLS102 Internal error in APC kernel" in SAP typically indicates an issue with the Analytics Cloud (SAC) or the underlying data processing components. This error can arise due to various reasons, including configuration issues, data model problems, or system performance issues.
  Possible Causes:
  
  Data Model Issues: There may be inconsistencies or errors in the data model being used in the Analytics Cloud.
  Performance Problems: The system may be experiencing performance issues, such as high load or insufficient resources.
  Configuration Errors: Incorrect configuration settings in the Analytics Cloud or the underlying data sources can lead to this error.
  Version Compatibility: There may be compatibility issues between different versions of SAP components or between SAP Analytics Cloud and other SAP systems.
  Corrupted Data: Corrupted or invalid data in the source systems can also trigger this error.
  
  Solutions:
  
  Check Data Models: Review the data models in the Analytics Cloud for any inconsistencies or errors. Ensure that all data