Question

A data engineer is preparing a customer transactions dataset in AWS Glue DataBrew for an ML churn model. The 'signup_date' column contains values in several inconsistent string formats (e.g., '01/15/2023', '2023-01-15', 'Jan 15 2023') because the data was merged from three legacy systems. Before the dates can be used to engineer tenure features, they must be standardized into a single consistent date type. What is the most efficient way to accomplish this within the data preparation workflow?

Accepted Answer

Apply a DataBrew date-format transformation that parses the source patterns and outputs a single standardized date format for the column.

Answer

Drop all rows where the 'signup_date' does not already match the ISO 8601 format to guarantee consistency.

Answer

Export the raw column to Amazon S3 and use Amazon Macie to reformat the date strings before re-importing.

Answer

Leave the dates as strings and rely on the SageMaker training algorithm to automatically infer and normalize the formats.