You need ads data to serve AI models and historical data for analytics. Longtail and outlier data points need to be identified. You want to cleanse the data in near-real time before running it through AI models. What should you do?

A. Use Cloud Storage as a data warehouse, shell scripts for processing, and BigQuery to create views for desired datasets. B. Use Dataflow to identify longtail and outlier data points programmatically, with BigQuery as a sink. C. Use BigQuery to ingest, prepare, and then analyze the data, and then run queries to create views. D. Use Cloud Composer to identify longtail and outlier data points, and then output a usable dataset to BigQuery.