Automated MAPs/SETs Update with Reference Data Handling

Problem Statement: Currently, updating MAPs/SETs in IBM requires a manual process of exporting reference data, updating MAPs/SETs, publishing new MAPs/SETs, and loading data back. This process is time-consuming and error-prone.

Proposed Solution: Implement an automated function that can handle incremental data changes within MAPs/SETs while maintaining associated reference data.

Function Steps:

1. Data Removal:

   • Identify Reference Data: Determine the associated reference dataset for the given MAPs/SETs.

   • Temporary Storage: Create a temporary storage location (e.g., a staging table or file) to store the existing reference data.

   • Data Export: Export the reference data from the original dataset to the temporary storage.

2. MAPs/SETs Update:

   • Apply New MAPs/SETs: Replace the existing MAPs/SETs with the new ones.

   • RDM-Specific Updates: Implement customized steps for updating RDM MAPs/SETs if necessary.

3. Publish and Validate:

   • Publish MAPs/SETs: Publish the updated MAPs/SETs to the appropriate environment.

   • Error Checking: Validate the published MAPs/SETs to ensure there are no errors or inconsistencies.

4. Data Restoration:

   • Load Data: Load the reference data from the temporary storage back into the original dataset, aligning it with the updated MAPs/SETs.

5. Publish Dataset:

   • Publish Dataset: Publish the updated dataset containing the new MAPs/SETs and restored reference data.

Benefits of Automation:

• Efficiency: Reduce manual effort and time spent on the update process.

• Accuracy: Minimize the risk of errors during data transfer and updates.

• Consistency: Ensure consistent application of updates across different MAPs/SETs.

• Scalability: Handle updates for large datasets and complex scenarios.

Additional Considerations:

• Error Handling: Implement robust error handling mechanisms to prevent data loss or corruption.

• Performance Optimization: Optimize the function for efficient processing, especially for large datasets.