AWS Certified Solutions Architect - Associate
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A company has an Amazon S3 data lake that is governed by AWS Lake Formation. The company wants to create a visualization in Amazon QuickSight by joining the data in the data lake with operational data that is stored in an Amazon Aurora MySQL database. The company wants to enforce column-level authorization so that the company's marketing team can access only a subset of columns in the database.
Which solution will meet these requirements with the LEAST operational overhead?
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UAnonymous
A
Use Amazon EMR to ingest the data directly from the database to the QuickSight SPICE engine. Include only the required columns.
B
Use AWS Glue Studio to ingest the data from the database to the S3 data lake. Attach an IAM policy to the QuickSight users to enforce column-level access control. Use Amazon S3 as the data source in QuickSight.
C
Use AWS Glue Elastic Views to create a materialized view for the database in Amazon S3. Create an S3 bucket policy to enforce column-level access control for the QuickSight users. Use Amazon S3 as the data source in QuickSight.
D
Use a Lake Formation blueprint to ingest the data from the database to the S3 data lake. Use Lake Formation to enforce column-level access control for the QuickSight users. Use Amazon Athena as the data source in QuickSight.
Explanation:
Explanation
Correct Answer: D
Why Option D is correct:
- Lake Formation Blueprint: AWS Lake Formation provides blueprints that simplify data ingestion from various sources including Amazon Aurora databases. This reduces operational overhead compared to manually configuring EMR or Glue jobs.
- Lake Formation Column-Level Security: Lake Formation natively supports fine-grained access control including column-level security. You can define permissions at the column level for specific users or groups.
- Athena Integration: Using Amazon Athena as the data source in QuickSight allows you to query data from the S3 data lake while leveraging Lake Formation's security controls. Athena respects Lake Formation permissions.
- Least Operational Overhead: This approach leverages managed services (Lake Formation, Athena) with built-in security controls, minimizing the need for custom IAM policies or S3 bucket policies.
Why other options are incorrect:
A. Amazon EMR:
- EMR requires significant operational overhead for cluster management
- SPICE engine doesn't support dynamic column-level security - you'd need to create separate datasets for different permission levels
- Not integrated with Lake Formation's governance model
B. AWS Glue Studio + IAM policies:
- IAM policies don't support column-level granularity for S3 data
- Would require complex S3 object tagging and IAM condition keys
- Manual management of IAM policies increases operational overhead
C. AWS Glue Elastic Views + S3 bucket policies:
- S3 bucket policies don't support column-level access control
- Would require creating separate S3 objects for different column subsets
- Materialized views need to be refreshed, adding operational complexity
Key AWS Services Integration:
- Lake Formation: Centralized data governance with fine-grained access control
- Athena: Serverless query service that respects Lake Formation permissions
- QuickSight: Can connect to Athena and inherit Lake Formation security
This solution provides a fully managed approach with minimal operational overhead while meeting all security requirements.
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