An Azure Data Factory author needs two kinds of data preparation: a visual transformation graph that runs at scale on Spark, and an interactive, spreadsheet-style cleanup experience for a business user. Which statement correctly describes these two options?
Show answer and explanation
Answer: C
Mapping Data Flows let authors build transformations visually without code, and Data Factory executes them as Spark jobs at scale. Wrangling Data Flows, implemented as the Power Query activity, give users an interactive, spreadsheet-like experience based on Power Query Online for shaping data. They solve different problems: scalable transformation logic versus hands-on exploration and cleanup. Treating them as identical overlooks that difference.
Why the other options are wrong
- A. Neither option requires hand-written Python. Mapping Data Flows are configured visually, and Wrangling Data Flows use a point-and-click Power Query interface, so this statement is false.
- B. Wrangling Data Flows do not run Scala, and Mapping Data Flows do not depend on SQL statements. Both are configured through the Data Factory interface rather than through these languages.
- D. The two features serve different purposes. Mapping Data Flows run Spark transformations at scale, while Wrangling Data Flows offer interactive Power Query preparation, so they are not identical.