A customer ID is stored as a string in one S3 dataset and an integer in another.
Prevent missing matches and understand the cost of conversion.
Which approach is most appropriate?
Standardize the schema and use an explicit CAST during migration while checking nulls, padding, and invalid values.
Detailed explanation
Lifecycle rules transition or expire objects and do not change values.
Lifecycle rules transition or expire objects and do not change values.
Leading zeros, invalid values, and null behavior can cause missed matches or failures.
Leading zeros, invalid values, and null behavior can cause missed matches or failures.
A Cartesian product creates unnecessary data and cost.
A Cartesian product creates unnecessary data and cost.
The join compares values with the same meaning and known conversion behavior.
The join compares values with the same meaning and known conversion behavior.
Check official documentation
Compare the following points with the official documentation.
Amazon Athena CAST、JOIN、data type conversionの公式説明を確認する。
What to confirm
暗黙変換と明示的型統一による結合品質の違いを説明できる。
Key points
- JOIN
- CAST
- Data type
- Leading zero
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
Key quality
Normalize nulls, whitespace, case, and padding and measure unmatched rows.
Join cost
Filter and project early to reduce data shuffled or compared by the join.