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Answer: Utilize Lake Formation blueprint for data ingestion into S3 from the database, enforce column-level access control with Lake Formation, and use Amazon Athena as the QuickSight data source.
The correct answer is D. Using a Lake Formation blueprint to ingest the data from the database to the S3 data lake and enforcing column-level access control with Lake Formation ensures that access policies are centralized and consistent. By using Amazon Athena as the data source in QuickSight, the solution leverages serverless querying capabilities with minimal operational overhead. Options A, B, and C introduce more complexity either through additional data processing steps or the need for creating and managing custom policies, which do not align with the requirement for the least operational overhead.
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A company utilizes AWS Lake Formation to manage an Amazon S3 data lake and seeks to integrate visualizations in Amazon QuickSight by merging data from the data lake with Amazon Aurora MySQL operational data. The company requires column-level access control for the marketing team, restricting access to specific columns. Identify the solution with minimal operational overhead for this requirement.
A
Ingest data from the database directly into QuickSight SPICE, filtering to include only necessary columns using Amazon EMR.
B
Leverage AWS Glue Studio for data ingestion into S3, enforce column-level access via IAM policies for QuickSight users, with S3 serving as the data source in QuickSight.
C
Employ AWS Glue Elastic Views to generate a materialized view in S3 from the database, apply an S3 bucket policy for column-level access control, and utilize S3 as the QuickSight data source.
D
Utilize Lake Formation blueprint for data ingestion into S3 from the database, enforce column-level access control with Lake Formation, and use Amazon Athena as the QuickSight data source.