An analyst wants to explore partitioned S3 data interactively with Spark DataFrames and visualizations.
Avoid creating and terminating an EMR cluster just for exploration and use a notebook workflow.
Which service and configuration are most appropriate?
Create an Amazon Athena workgroup with the Apache Spark engine and use an Athena notebook for serverless, interactive analysis.
Detailed explanation
A SQL engine workgroup is not the Spark notebook execution environment.
A SQL engine workgroup is not the Spark notebook execution environment.
A crawler registers schemas; it does not execute interactive Spark analysis or visualization.
A crawler registers schemas; it does not execute interactive Spark analysis or visualization.
The notebook runs Spark cells interactively and avoids user-managed cluster provisioning and patching.
The notebook runs Spark cells interactively and avoids user-managed cluster provisioning and patching.
Each session adds cluster provisioning, patching, and termination work, contrary to the low-operations requirement.
Each session adds cluster provisioning, patching, and termination work, contrary to the low-operations requirement.
Check official documentation
Compare the following points with the official documentation.
Amazon Athena for Apache Sparkのノートブック、Sparkワークグループ、セッションの公式説明を確認する。
Official references
What to confirm
SQLクエリエンジン、Glue Crawler、クラスター管理型Sparkと、Athena Sparkノートブックの違いを説明できる。
Key points
- Athena for Apache Spark
- Spark workgroup
- Notebook
- Serverless
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 the relevant DEA-C01 Skill and current AWS service documentation.
Foundation review
Interactive exploration
Use cells to inspect, transform, and visualize data incrementally.
Serverless analysis
Run on demand without managing a persistent cluster lifecycle.