A customer master and order data are stored in S3 and must be joined by customer ID.
Keep unmatched orders for investigation while producing a clean analytical table.
Which transformation design is most appropriate?
Use a Glue Join transformation and route matched and unmatched records to separate outputs.
Detailed explanation
The join key can be specified and matched rows can be separated from unmatched rows for downstream processing.
The join key can be specified and matched rows can be separated from unmatched rows for downstream processing.
Manual editing is not reproducible or scalable for a scheduled ETL process.
Manual editing is not reproducible or scalable for a scheduled ETL process.
Lifecycle rules manage object retention and storage transitions; they do not join datasets.
Lifecycle rules manage object retention and storage transitions; they do not join datasets.
CloudTrail provides AWS API audit information, not a join of S3 datasets.
CloudTrail provides AWS API audit information, not a join of S3 datasets.
Check official documentation
Compare the following points with the official documentation.
AWS GlueのJoin変換、DynamicFrame、エラー行処理を確認する。
What to confirm
データ結合とストレージ管理・監査検索の違いを説明できる。
Key points
- Glue Join
- Join key
- Unmatched records
- Data quality
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
Join cardinality
Key uniqueness and duplicate rows determine whether a join expands or reduces the output.
Quality path
Keeping unmatched rows preserves evidence for correction instead of silently dropping them.