Directly stream all files to BigQuery and use scheduled DLP API scans to identify and classify PII. This approach leverages BigQuery's analytics capabilities but may expose PII during the scanning interval.

Implement a two-bucket system: 'Processing' and 'Archived'. Stream files to the 'Processing' bucket, apply DLP API scans in real-time, and then move files to 'Archived' based on scan results. This reduces exposure time but may increase costs due to real-time scanning.

Use a single bucket for all files, applying DLP API scans periodically. Files identified as containing PII are then encrypted. This method is simple but leaves PII exposed until the scan completes.

Establish a three-bucket system: 'Quarantine', 'Sensitive', and 'Non-sensitive'. All incoming files are initially placed in 'Quarantine'. Periodic DLP API scans then move files to 'Sensitive' or 'Non-sensitive' based on content, ensuring PII is never exposed in an unsecured state.

Combine streaming files directly into BigQuery with real-time DLP API scans before storage. This ensures immediate PII detection but may not be cost-effective for high-volume data streams.