When the authors have new book releases, there is often an increase in sales activity. This increase slows the data ingestion process. The company's sales team reports that during the last month…

Splitting a dataflow might help with parallelism, but it does not inherently minimize the total volume of data transferred during the ingestion process.

Configuring a scheduled refresh ensures regularity but does not minimize data transfer if it performs a full refresh each time, potentially exacerbating performance issues for large datasets.

'Store rows from the past' defines the historical data retention policy for the dataflow, not the window for which data is refreshed incrementally, thus it doesn't directly address minimizing data transfer during refresh operations.

Setting 'Refresh rows from the past' to 1 Year would process a much larger dataset than necessary for daily updates, leading to increased data transfer and longer refresh times, which contradicts the requirement to minimize data transferred.

Configuring incremental refresh is the most effective solution to minimize the amount of data transferred and improve refresh times, especially for frequently updated datasets. Setting 'Refresh rows from the past' to 1 Month ensures that only recent, relevant data is processed, making the data up-to-date daily while avoiding re-processing of unchanged historical data.