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Status Submitted
Created by Guest
Created on Sep 9, 2024

Automated Reference Data Cleanup and Versioning Maintenance

Problem Statement: Currently, IKC creates a new version for each updated dataset, leading to the accumulation of older versions. There is no built-in mechanism to automatically purge older versions or retain only a specific number of recent versions.

Proposed Solution: Implement an automated data cleanup and versioning mechanism for IKC-RDM (Reference Data Management) , allowing for the configuration and retention of older versions.

Function Requirements:

  • Version Retention Policy:

    • Define a retention policy that specifies the number of versions to keep for each dataset.

    • Implement a mechanism to automatically purge older versions that exceed the retention limit.

  • Version Tagging:

    • Determine whether IKC tags the entire dataset as a new version or only the changed data.

    • If IKC tags only the changed data, implement a CDC-like process to track changes and tag accordingly.

  • Data Cleanup:

    • Provide a mechanism to purge older versions of data, either automatically based on the retention policy or manually.

  • Configuration Management:

    • Allow for the configuration of version retention policies and data cleanup settings for each dataset.

    • Maintain these configurations in source control to ensure consistency and traceability.

Benefits of Automated Data Cleanup and Versioning:

  • Storage Optimization: Reduce storage usage by purging older versions of data that are no longer needed.

  • Performance Improvement: Improve query performance by reducing the number of versions to search through.

  • Data Governance: Implement a data governance framework to manage data lifecycle and retention policies.

  • Compliance: Ensure compliance with data retention and privacy regulations.

Needed By Quarter