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Status Submitted
Workspace Knowledge Catalog
Created by Guest
Created on Sep 9, 2024

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.

Needed By Quarter