You need a programmatic, cost-effective pipeline to migrate on-premises data into BigQuery with optional streaming or batch modes and mask sensitive fields before loading. Which approach meets these requirements?

Building a Dataflow pipeline with Apache Beam Python SDK is the most flexible and cost-effective solution. Dataflow supports both batch and streaming modes natively, allowing you to choose based on your migration needs. Integrating Cloud DLP directly into the pipeline ensures sensitive data is masked before it lands in BigQuery, meeting the security requirement. This programmatic approach offers fine-grained control over the entire process. Cloud Data Fusion is a good option for ETL, but it might be overkill for a direct migration with specific DLP requirements and could be less cost-effective for simple migrations. BigQuery Data Transfer Service is for scheduled, recurring transfers from supported sources and doesn't inherently support pre-load data masking or custom transformations like DLP integration during transfer. Datastream is primarily for continuous data replication (CDC) from databases and doesn't directly offer pre-load masking with Cloud DLP.