Error
Error Code:
4672
SAP S/4HANA Error 4672: Data Provisioning Failure
Description
This error indicates a general issue with data provisioning services within SAP S/4HANA. It typically occurs when the system attempts to retrieve, process, or synchronize data from various sources, but an underlying service or configuration prevents successful completion.
Error Message
ERR_DATAPROV
Known Causes
3 known causesMisconfigured Data Source
Data provisioning relies on correctly configured data sources, connections, and transformation rules. Incorrect settings can lead to failures.
System Connectivity Issues
Problems with network connectivity between SAP S/4HANA and the data source, or an unresponsive data provisioning service, can prevent data flow.
Insufficient Permissions
The user or system account attempting data provisioning may lack the necessary authorizations to access the data source or perform the required operations.
Solutions
4 solutions available1. Verify Data Source Connectivity and Credentials easy
Ensures the S/4HANA system can connect to the underlying data source and uses valid credentials.
1
Identify the data source involved in the provisioning failure. This could be a HANA database, an external database, or a data lake.
2
Navigate to the relevant data provisioning tool or transaction in S/4HANA. For HANA, this might involve checking the SAP HANA cockpit or HANA Studio.
3
Locate the connection settings for the identified data source. This typically involves checking RFC destinations (SM59), database connection configurations, or cloud connector settings.
4
Test the connection using the configured credentials. If the test fails, update the credentials (username, password, tokens) and re-test.
5
If using a cloud connector, ensure it is running and properly configured to bridge S/4HANA to the external data source. Check its status and logs.
2. Check Data Source Availability and Resource Utilization medium
Confirms the data source is operational and not experiencing performance issues that could lead to provisioning failures.
1
Access the monitoring tools of the target data source (e.g., HANA Studio, cloud provider monitoring dashboards, database administration tools).
2
Verify that the data source is online and accessible. Check for any ongoing maintenance or unplanned downtime.
3
Monitor the resource utilization of the data source, including CPU, memory, disk I/O, and network bandwidth. High utilization can cause timeouts and provisioning errors.
4
If resource utilization is high, investigate the cause. This might involve identifying resource-intensive queries, background processes, or insufficient hardware.
5
Consider scaling up the data source resources or optimizing existing workloads to alleviate performance bottlenecks.
3. Review Data Provisioning Job Logs and Error Details medium
Examines detailed logs to pinpoint the exact cause of the data provisioning failure.
1
In S/4HANA, navigate to the transaction or application that initiated the data provisioning process. This could be related to BW/4HANA, SAP Data Services, or native HANA modeling.
2
Locate the job or process that failed with error code 4672. Access its detailed logs.
3
Analyze the log entries for more specific error messages. These messages often provide clues about the underlying issue, such as missing tables, incorrect data types, authorization problems, or network interruptions.
4
If the error relates to SQL statements, extract the problematic SQL and test it directly against the data source to identify syntax errors or logical issues.
-- Example of extracting and testing SQL:
-- SELECT * FROM YOUR_FAILED_TABLE WHERE ...5
Consult SAP Notes and documentation using keywords from the detailed error messages to find known issues and their resolutions.
4. Validate Schema and Data Type Mismatches medium
Ensures that the schema and data types in the source and target systems are compatible.
1
Compare the schema definitions of the source and target data objects involved in the provisioning process. Pay close attention to table structures, column names, and data types.
2
Specifically check for discrepancies in data types. For example, a VARCHAR in the source might be mapped to an INT in the target, causing conversion errors.
3
Verify that all required columns exist in both the source and target. Missing columns can lead to provisioning failures.
4
If data type conversions are expected, ensure they are correctly configured in the data provisioning tool or mapping settings.
5
If necessary, adjust the schema in either the source or target system, or modify the data provisioning mappings to align the data structures.