Golden Age Datasets - Data Versioning Strategy¶

Core Versioning Principles¶

Semantic Versioning for Healthcare Data¶

Semantic versions (MAJOR.MINOR.PATCH) provide clear communication about the nature and impact of data changes:

MAJOR: Breaking changes that affect data structure, schema compatibility, or data interpretation
MINOR: Backward-compatible enhancements, new fields, or extended functionality
PATCH: Bug fixes, data corrections, or non-functional improvements

Version Bumping Guidelines¶

Bump version when any change is made to the dataset or its metadata
Update data card with detailed change documentation for every version
Add new schema file when introducing breaking changes to maintain backward compatibility

Data Versioning Workflow¶

Version Creation Process¶

graph TD
    A[Data Change Request] --> B{Change Type?}
    B -->|Breaking| C[MAJOR Version Bump]
    B -->|Feature Addition| D[MINOR Version Bump]
    B -->|Bug Fix| E[PATCH Version Bump]
    C --> F[Update Schema Registry]
    D --> F
    E --> F
    F --> G[Update Data Card]
    G --> H[Generate Version Metadata]
    H --> I[Deploy Version]
    I --> J[Archive Previous Version]

Pre-Deployment Checklist¶

Schema validation passes for all dependent systems
Data card updated with change rationale and impact assessment
Breaking changes communicated to all stakeholders
Rollback plan documented and tested
Compliance review completed for healthcare data changes

Schema Evolution Strategy¶

Breaking Changes Requiring MAJOR Version¶

Structural Changes: Modifying primary key structures or relationship cardinalities
Data Type Changes: Converting data types that affect interpretation (e.g., integer to string for IDs)
Required Field Removal: Eliminating fields that downstream systems depend on
Validation Rule Changes: Modifications that would invalidate existing valid data
Semantic Changes: Altering the meaning or interpretation of existing fields

Non-Breaking Changes (MINOR Version)¶

New Optional Fields: Adding fields that don't affect existing data consumers
Extended Enumerations: Adding new valid values to existing categorical fields
Metadata Enhancements: Additional documentation or descriptive fields
Performance Optimizations: Index changes or data organization improvements

Patch-Level Changes (PATCH Version)¶

Data Corrections: Fixing erroneous values while maintaining data types
Documentation Updates: Clarifying field descriptions or usage guidelines
Validation Bug Fixes: Correcting schema validation rules
Metadata Corrections: Fixing typos or inaccuracies in data documentation

Healthcare-Specific Versioning Considerations¶

PHI and Patient Safety¶

Patient Safety Reviews: All changes affecting clinical data require safety review
PHI Impact Assessment: Evaluate how changes affect protected health information handling
Clinical Workflow Compatibility: Ensure changes don't break existing care processes
Emergency Access Preservation: Verify emergency access patterns remain functional

Compliance and Audit Requirements¶

HIPAA Compliance Review: All schema changes must maintain HIPAA compliance
Audit Trail Preservation: Version changes must not break audit trail continuity
Data Retention Compliance: Ensure version changes support retention requirements
Breach Notification Assessment: Evaluate if changes affect breach notification obligations

Version Metadata and Documentation¶

Data Card Requirements¶

Each version must include a comprehensive data card with:

version_metadata:
  version: "2.1.3"
  release_date: "2024-01-15"
  change_type: "MINOR"  # MAJOR, MINOR, PATCH
  change_summary: "Added optional patient_preferred_name field"
  impact_assessment:
    breaking: false
    affected_systems: ["patient_registration", "care_coordination"]
    migration_required: false
  schema_files:
    - "schemas/v2.1.3/patient_schema.json"
    - "schemas/v2.1.3/care_plan_schema.json"
  validation_rules:
    - "JSON Schema Draft 7 compliance"
    - "HIPAA PHI field validation"
    - "Custom business rule validation"

Schema Registry Management¶

Immutable Schema Storage: Previous schema versions remain accessible
Schema Compatibility Matrix: Document which versions are compatible
Migration Path Documentation: Clear upgrade paths between versions
Deprecation Notices: Timeline for retiring old schema versions

Version Deployment and Rollback¶

Staged Deployment Strategy¶

Development Environment: Schema changes tested with sample data
Staging Environment: Integration testing with dependent systems
Pilot Deployment: Limited production rollout with monitoring
Full Deployment: Complete rollout with rollback capability
Post-Deployment Monitoring: Performance and compatibility monitoring

Rollback Procedures¶

# Emergency rollback script
rollback_version() {
    local target_version=$1
    local current_version=$(get_current_version)

    echo "Rolling back from $current_version to $target_version"

    # 1. Validate rollback compatibility
    validate_rollback_path $current_version $target_version

    # 2. Create database backup
    create_backup $current_version

    # 3. Deploy previous version
    deploy_schema $target_version

    # 4. Migrate data if needed
    migrate_data_if_required $current_version $target_version

    # 5. Verify system health
    verify_system_health
}

Rollback Readiness Requirements¶

Database Backups: Automated daily backups with point-in-time recovery
Schema Compatibility: Clear understanding of which versions can be rolled back
Data Migration Scripts: Bidirectional migration capabilities when possible
Monitoring Dashboards: Real-time visibility into system health during rollback

Data Lineage and Provenance¶

Version Chain Tracking¶

Each dataset version maintains a complete lineage trail:

{
  "version": "3.2.1",
  "parent_versions": ["3.1.5", "3.2.0"],
  "derived_from": [
    {
      "source_dataset": "patient_demographics",
      "source_version": "2.4.1",
      "transformation": "phi_removal_anonymization"
    }
  ],
  "change_log": [
    {
      "version": "3.2.0",
      "change_type": "MINOR",
      "description": "Added emergency_contact_priority field",
      "date": "2024-01-10",
      "author": "data-team@goldenagetech.us"
    },
    {
      "version": "3.2.1",
      "change_type": "PATCH",
      "description": "Fixed validation regex for phone numbers",
      "date": "2024-01-12",
      "author": "data-team@goldenagetech.us"
    }
  ]
}

Provenance Requirements¶

Data Source Tracking: Complete chain of custody for all data
Transformation Logging: Record of all data transformations and algorithms
Quality Metrics: Track data quality metrics across versions
Compliance Audit Trail: Maintain audit trail for regulatory compliance

Automated Versioning Tools¶

Version Management Scripts¶

# Automated version bumping
bump_version() {
    local bump_type=$1  # MAJOR, MINOR, PATCH
    local reason=$2

    # 1. Determine new version number
    local new_version=$(calculate_next_version $bump_type)

    # 2. Update version files
    update_version_files $new_version

    # 3. Generate data card
    generate_data_card $new_version "$reason"

    # 4. Create schema snapshot
    snapshot_current_schema $new_version

    # 5. Commit changes
    commit_version_changes $new_version "$reason"
}

Continuous Integration Pipeline¶

# CI/CD pipeline for data versioning
stages:
  - validate_schema
  - test_compatibility
  - generate_documentation
  - deploy_version
  - integration_tests
  - compliance_checks

validation_rules:
  - schema_draft7_compliance: required
  - backward_compatibility: required_for_minor_patch
  - hipaa_compliance: required
  - data_quality_thresholds: required

Best Practices for Healthcare Data¶

Clinical Data Considerations¶

Patient Safety First: All changes must preserve or enhance patient safety
Clinical Workflow Compatibility: Changes must not disrupt existing care processes
Emergency Access: Ensure emergency access patterns work across all versions
Interoperability: Maintain compatibility with healthcare data exchange standards

Data Quality Management¶

Quality Gates: Automated quality checks before version deployment
Quality Metrics: Track completeness, accuracy, and consistency metrics
Quality Baselines: Establish minimum quality thresholds for each version
Quality Regression Testing: Ensure changes don't degrade existing data quality

Performance Optimization¶

Query Performance: Monitor and optimize query performance across versions
Storage Efficiency: Track storage requirements and compression effectiveness
Access Patterns: Optimize for common healthcare data access patterns
Caching Strategies: Implement appropriate caching for frequently accessed data

Version Deprecation and Retirement¶

Deprecation Timeline¶

Active Support: Full support and updates (current + 2 previous versions)
Maintenance Support: Security updates and critical bug fixes (up to 1 year after active support)
End of Life: No further support (2 years after initial release)

Retirement Process¶

Announcement: 6 months notice before retirement
Migration Period: Support migration tools and documentation
Final Notice: 30 days before final removal
Archival: Move to long-term archival storage
Access Removal: Remove from active systems and APIs

Emergency Procedures¶

Critical Issue Response¶

graph TD
    A[Critical Issue Detected] --> B[Assess Impact]
    B -->|Patient Safety Risk| C[Immediate Rollback]
    B -->|Data Corruption| D[Emergency Recovery]
    B -->|Performance Issue| E[Quick Fix Deployment]
    C --> F[Restore Previous Version]
    D --> F
    E --> F
    F --> G[Root Cause Analysis]
    G --> H[Preventive Measures]

Emergency Contacts¶

Data Emergency: data-emergency@goldenagetech.us
Clinical Safety: clinical-safety@goldenagetech.us
Compliance Emergency: compliance-emergency@goldenagetech.us

Monitoring and Analytics¶

Version Performance Metrics¶

Adoption Rate: How quickly consumers migrate to new versions
Compatibility Issues: Number of compatibility problems reported
Performance Impact: Query performance changes across versions
Error Rates: Error rates before and after version deployment

Health Monitoring Dashboard¶

Real-time Metrics: Current version usage and performance
Trend Analysis: Version adoption and deprecation trends
Alert Thresholds: Automated alerts for unusual patterns
Capacity Planning: Storage and performance forecasting

This data versioning strategy ensures safe, compliant, and efficient evolution of healthcare datasets while maintaining backward compatibility and supporting clinical workflows.

Last Updated: 9/28/2025 Version: 1.0.0