A data analyst is tasked with analyzing a sales dataset imported from a CSV file. The analyst needs to calculate the total revenue by summing the SaleAmount column. However, the query fails with an error indicating a data type mismatch. Upon inspection, the SaleAmount column contains values formatted as strings with a currency symbol, such as '$1,250.75'. To perform the required calculation, which data transformation technique must be applied to the SaleAmount column first?
The correct answer is Conversion. The SUM() aggregate function requires a numeric data type (e.g., INTEGER, DECIMAL, FLOAT) to perform calculations. The SaleAmount column is currently a string (or text) data type because it contains non-numeric characters like the dollar sign ('$') and comma (','). Before the analyst can sum the values, they must first convert the column's data type from string to a numeric type. This process typically involves removing the non-numeric characters and then applying a CAST or CONVERT function.
Aggregation is the process of calculating a single value from a set of values, such as using SUM(). This is the ultimate goal, but it cannot be performed until the data type issue is resolved.
Parsing involves breaking down a string into its component parts. While one might parse the string to remove the '$', the fundamental operation required to fix the calculation error is the data type conversion itself.
Standardization is the process of transforming data to a common format or scale (e.g., z-score). While removing symbols to create a consistent format is related, the core task that enables mathematical functions is the conversion of the data type.
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