A source database NUMBER and date columns will be migrated to Aurora types.
Detect precision loss and time-zone changes before a safe cutover.
Which approach is most appropriate?
Define explicit type mappings and validate range, precision, nulls, timestamps, counts, and conversion errors before cutover.
Detailed explanation
The target type's range and meaning are checked, and unconvertible values are detected before production use.
The target type's range and meaning are checked, and unconvertible values are detected before production use.
An archive storage class does not provide database type mapping or CDC conversion.
An archive storage class does not provide database type mapping or CDC conversion.
Stringifying values can remove numeric and temporal constraints and break comparisons.
Stringifying values can remove numeric and temporal constraints and break comparisons.
Missing, rounded, or failed values can enter production data.
Missing, rounded, or failed values can enter production data.
Check official documentation
Compare the following points with the official documentation.
AWS SCT data type mapping、AWS DMS transformation rules、data validationの公式説明を確認する。
What to confirm
型変換を無視した移行と、意味を保つ検証手順の違いを説明できる。
Key points
- Type mapping
- Precision
- Time zone
- Data validation
Notes
- Environment: AWS公式DEA-C01試験ガイドと各サービスの公式ドキュメントの確認
- Confirm current AWS service behavior in the official DEA-C01 documentation.
Foundation review
Semantic preservation
A target type must preserve calculations, ordering, null behavior, and time meaning.
Cutover quality
Boundary samples and aggregate comparisons complement simple row-count checks.