Data scientists need a low‑code way to explore, cleanse, and validate files in Cloud Storage. Which solution provides that capability?

The correct answer is to give the team access to Dataprep. Dataprep by Trifacta is a serverless, intelligent, and visual service for exploring, cleansing, and preparing data for analysis. It provides a low-code, interactive interface that allows data scientists to visually profile data, apply transformations, and validate data quality directly from Cloud Storage, making it ideal for the described use case. Giving the team Dataflow access would require writing code (Java, Python, or SQL with Dataflow SQL), which contradicts the "low-code" requirement. Creating external tables in BigQuery and using SQL transforms is a valid approach for data manipulation, but it's primarily SQL-based and might not offer the same visual, interactive data exploration and profiling capabilities as Dataprep for initial cleansing and validation. Loading files into BigQuery first and then using SQL for transformation is also SQL-centric and adds an extra step of loading data into BigQuery before exploration and cleansing, which Dataprep can do directly from Cloud Storage.