QuestionQ234

Data Security and Governance

A company must implement a data mesh architecture in which the trading, risk, and compliance domains each own their data. The teams must share specific views with one another. The teams have more than 1,000 tables across 50 databases in the AWS Glue Data Catalog. All three teams use Amazon Athena for on-demand analysis. The teams use Amazon Redshift to produce complex reports. The compliance team must audit all data access. Access to personally identifiable information (PII) data must be restricted.

The company needs a scalable solution that meets the team requirements. The solution must support analysis across team domains.

Which solution meets these requirements?

  • A Create views in Athena for on-demand analysis. Use the Athena views in Amazon Redshift to perform cross-domain analytics. Use AWS CloudTrail to audit data access. Use AWS Lake Formation to establish fine-grained access control.
  • B Use AWS Glue Data Catalog views to perform analysis. Use AWS CloudTrail logs to audit data access. Use AWS Lake Formation to manage access permissions. Use security definer views to mask PII.
  • C Use AWS Lake Formation to set up cross-domain access to tables. Set up fine-grained access controls.
  • D Create materialized views and enable Amazon Redshift datashares for each domain. Configure cross-domain access policies.
Explanation

AWS Glue Data Catalog views are multi-dialect, security-definer views that provide one governed view schema across supported analytics engines, including Amazon Athena and Amazon Redshift. They can limit the exposed columns or filter data, allowing PII to be masked while consumers access the view without direct access to the underlying tables. AWS Lake Formation grants and enforces fine-grained permissions on Data Catalog resources, and its CloudTrail integration provides audit logs of data access. This supports scalable, governed sharing of domain-specific views across the large catalog.

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