You want to rebuild a slow PySpark batch pipeline using a serverless, SQL-based approach. Raw data is in Cloud Storage. How should you implement the pipeline to speed development and runtime?

The correct option leverages BigQuery's fully managed, serverless, and SQL-based nature for both development and runtime speed. Loading data directly into BigQuery from Cloud Storage is efficient. Translating PySpark transformations to BigQuery SQL allows for direct execution within BigQuery's optimized engine, eliminating the need to manage Spark clusters and significantly speeding up query execution for batch processing. Writing results to a new BigQuery table keeps the entire process within a single, high-performance ecosystem. Converting PySpark to SparkSQL on Dataproc still involves managing a Spark environment, which isn't fully serverless. Ingesting into Cloud SQL introduces an unnecessary relational database step and doesn't align with a serverless, SQL-based approach for large-scale analytics. Reimplementing with Apache Beam Python SDK, while serverless, is not SQL-based, which goes against the requirement for a SQL-based approach to speed development.