A finance team in Microsoft Fabric must clean a daily CSV extract by trimming whitespace, splitting a full-name column, replacing error values, and merging a small lookup before landing the result in a Lakehouse table. The people who will own and edit this transformation are business analysts comfortable with Excel but not with Python or SQL, and the team wants them to maintain the logic visually without writing code. Which transformation engine best fits this team and workload?
- AA Dataflow Gen2 built in Power Query that applies the trims, splits, replacements, and merge steps visually and lands the result in the Lakehouse. Correct
- BA PySpark notebook that reads the CSV into a DataFrame and applies the trims, splits, replacements, and join before writing the Lakehouse table.
- CA T-SQL stored procedure in a Warehouse that uses string functions and a JOIN to shape the data and INSERT it into the target table.
- DA KQL query over an Eventhouse that parses and reshapes the rows before exporting the cleaned output back to the Lakehouse table.
Why A is correct: Dataflows Gen2 expose Power Query, a visual low-code mashup tool whose applied-steps list lets Excel-literate analysts trim, split, replace, and merge without code, matching both the workload and the owning team's skill set.
Why B is wrong: A PySpark notebook can do every step, but it requires Python skills the analysts do not have, so day-to-day maintenance would fall outside the owning team and break the stated visual, code-free requirement.
Why C is wrong: T-SQL handles the transformation logic well, yet it demands SQL fluency the analysts lack; the requirement is explicitly a visual, no-code maintenance experience, which a hand-written procedure does not provide.
Why D is wrong: KQL targets log and telemetry analytics, not visual file cleansing, and it is another code language unfamiliar to the analysts, so it fits neither the relational CSV workload nor the no-code maintenance goal.