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Answer: Establish a data lake with AWS Lake Formation, connect to Amazon RDS via AWS Glue JDBC, register the S3 bucket, and manage access with Lake Formation controls.
The correct answer is C. Creating a data lake using AWS Lake Formation allows for centralized data management with fine-grained access controls, which meets the company's requirement for managing permissions and minimizing operational overhead. AWS Lake Formation can directly utilize the data stored in Amazon S3 and Amazon RDS by creating a data catalog, enabling easy, secure access for analytics teams. Options A, B, and D either fail to minimize operational overhead or do not offer the same level of fine-grained access management as Lake Formation.
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An online retail firm with over 50M active customers and 25k+ daily orders stores purchase data in Amazon S3 and additional customer data in Amazon RDS. They seek a solution for analytics teams to access all data with fine-grained permissions and minimal operational overhead. Which solution meets these criteria?
A
Migrate purchase data to Amazon RDS, applying RDS access controls for permission management.
B
Use AWS Lambda to sync Amazon RDS to Amazon S3 periodically, implement an AWS Glue crawler, query with Amazon Athena, and enforce access limits with S3 policies.
C
Establish a data lake with AWS Lake Formation, connect to Amazon RDS via AWS Glue JDBC, register the S3 bucket, and manage access with Lake Formation controls.
D
Deploy an Amazon Redshift cluster, use AWS Lambda for regular data replication from S3 and RDS to Redshift, and restrict access using Redshift's access controls.
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