Automated RD Type Modification with Data Migration

Problem Statement: Currently, modifying RD Types in IBM-RDMs requires creating new RD Sets and RD Types, which can be inefficient and error-prone. Additionally, handling reference data during RD Type modifications involves manual steps, leading to potential inconsistencies.

Proposed Solution: Implement an automated function that allows for in-place modification of existing RD Types, including data migration considerations, to streamline the update process.

Function Steps:

1. Data Removal:

   • Identify Reference Data: Determine the associated reference dataset for the given RD Type.

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

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

2. RD Type Modification:

   • Apply New RD Type Definition: Update the existing RD Type with the new definition, including any changes to column types or structures.

   • Data Validation: Validate the modified RD Type to ensure it adheres to RDM schema and constraints.

3. Data Migration:

   • Map Old to New: Map the existing data columns to the new RD Type columns based on their definitions and relationships.

   • Data Conversion: Convert data values as necessary to accommodate changes in column types or formats.

   • Data Loading: Load the migrated data into the modified RD Type.

4. Publish and Validate:

   • Publish Data Sets: Publish the updated data sets containing the modified RD Type.

   • Error Checking: Validate the published data sets to ensure there are no errors or inconsistencies.

5. Data Restoration:

   • Load Data: Load the remaining data from the temporary storage back into the original dataset, aligning it with the modified RD Type.

   • Publish Dataset: Publish the updated dataset with the new RD Type and restored data.

Benefits of Automation:

• Efficiency: Reduce manual effort and time spent on RD Type modifications.

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

• Flexibility: Allow for in-place modifications without creating new RD Sets or RD Types.

• Consistency: Ensure consistent application of updates across different RD Types.

• Scalability: Handle modifications 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.