A Glue job aggregates raw S3 data into an analytical table.
During incident analysis, trace the aggregate back through its source and transformations.
Which approach is most appropriate?
Maintain catalog metadata and job relationships so datasets, schemas, transformations, and impact paths are traceable.
Detailed explanation
TTL does not manage catalog lineage or transformation history.
TTL does not manage catalog lineage or transformation history.
Dataset, schema, job, and input-output relationships support source and impact tracing.
Dataset, schema, job, and input-output relationships support source and impact tracing.
A simple path does not record transformation relationships or downstream use.
A simple path does not record transformation relationships or downstream use.
API records do not fully describe business transformations inside a job.
API records do not fully describe business transformations inside a job.
Check official documentation
Compare the following points with the official documentation.
AWS Glue Data Catalog、data lineage、AWS Lake Formationの公式説明を確認する。
What to confirm
API監査ログだけではなく、データセットと変換の関係を管理する必要性を説明できる。
Key points
- Data lineage
- Data Catalog
- Impact analysis
- Metadata
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
Lineage granularity
Table, column, job version, execution, and timestamp detail determine investigation value.
Operational evidence
Catalog lineage complements API audit logs but records different facts about transformations.