When performing an inner join while ignoring spaces in values, which method minimizes development effort?

The choice of fuzzy matching with merge is optimal for minimizing development effort in scenarios where you need to perform an inner join while disregarding spaces in values. Fuzzy matching involves using algorithms that can identify similar values that may not exactly match but can still be effectively grouped or related based on some criteria, such as ignoring spaces or other minor discrepancies.

When using the merge method, it allows for joining datasets based on key values, which can encompass a broader range of similarities, including minor variations in data representation like extra spaces. This method is generally more straightforward and requires less manual intervention and fewer lines of code when compared to using append operations, which would typically involve more complex data manipulations and concatenation tasks.

In contrast, other options like fuzzy matching with append and using lookup tables can entail more time-consuming setup and complex logic. These methods might require additional steps to prepare the data for merging or could involve intricate handling of how to process the resulting dataset, which can increase development effort unnecessarily. Thus, fuzzy matching with merge stands out as the most efficient approach under the given conditions.