Implement a single pipeline where all files are streamed directly to BigQuery, and use scheduled DLP API scans to identify and segregate PII data post-ingestion. This approach minimizes initial setup complexity but may delay PII detection.

Design a dual-bucket system where all incoming data is initially stored in a 'Non-sensitive' bucket. Periodic DLP API scans are performed to identify PII, which is then moved to a 'Sensitive' bucket. This method reduces initial scanning overhead but risks temporary PII exposure.

Utilize a real-time processing pipeline that writes data to BigQuery and simultaneously triggers DLP API scans during the ingestion process. This ensures immediate PII detection but may increase latency and cost due to the continuous scanning.

Establish a three-bucket architecture comprising 'Quarantine', 'Sensitive', and 'Non-sensitive' buckets. All incoming data is first placed in 'Quarantine', scanned by the DLP API, and then moved to the appropriate bucket based on the scan results. This method ensures PII is never exposed to unauthorized access but requires more initial setup.

Combine real-time DLP API scanning with batch processing for comprehensive coverage. Initially, scan data in real-time as it's ingested, and supplement with periodic batch scans to catch any missed PII. This approach offers thorough PII protection but at a higher operational cost.