A company wants to implement a data lake in the AWS Cloud. The company must ensure that only specific teams have access to sensitive data in the data lake. The company must have row-level access control for the data lake.
Options:
A.
Use Amazon RDS to store the data. Use IAM roles and permissions for data governance and access control.
B.
Use Amazon Redshift to store the data. Use IAM roles and permissions for data governance and access control.
C.
Use Amazon S3 to store the data. Use AWS Lake Formation for data governance and access control.
D.
Use AWS Glue Catalog to store the data. Use AWS Glue DataBrew for data governance and access control.
A. RDS:Suitable for relational databases but does not provide native support for data lakes or row-level access.
B. Redshift:Primarily for analytics, not designed for large-scale data lake governance.
C. S3 + Lake Formation:Provides native support for data lakes with granular access control, including row-level permissions.
D. Glue Catalog + DataBrew:Focused on data preparation and metadata management, not row-level access control.
[References:AWS Lake Formation, , , , , ]
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