You are working at a sports association whose members range in age from 8 to 30. The association collects a large amount of health data, such as sustained injuries. You are storing this data in BigQuery. Current legislation requires you to delete such information upon request of the subject. You want to design a solution that can accommodate such a request. What should you do?

The most straightforward and compliant way to handle data deletion requests is to use a unique identifier for each individual and then delete all associated rows when a request is made. BigQuery supports DELETE statements, making this an efficient operation. Using the Data Loss Prevention (DLP) API and Data Catalog is valuable for identifying and classifying sensitive data but doesn't directly facilitate deletion upon request. It's more for discovery and protection. Creating a view to exclude data doesn't actually delete the data; it just hides it, which doesn't meet the legal requirement for deletion. Overwriting the unique identifier with a salted SHA256 value also doesn't delete the data. While it anonymizes the identifier, the rest of the personal health data would still exist and be linked to that individual, even if indirectly, which doesn't fulfill a deletion request.