In data processing, what is the key aspect when performing the 'Transform' stage of ETL?

The key aspect when performing the 'Transform' stage of ETL (Extract, Transform, Load) is that data is altered or cleansed to prepare for analysis. This stage involves various processes such as data cleaning, filtering, joining, aggregating, and converting data into a suitable format. The purpose of transformation is to ensure that the data being analyzed is accurate, consistent, and formatted correctly for the intended analysis or reporting.

Transforming data may include actions like removing duplicates, correcting errors, standardizing formats, and enriching data by combining it with relevant information from other sources. This step is crucial because the quality of the analysis depends heavily on the quality of the data being used; thus, adequately transformed data leads to more reliable insights and better decision-making.

While other options may involve actions related to data handling, they do not accurately reflect the transformative processes that are central to the ETL 'Transform' stage.