A company receives CSV files with address fields Door_No, Street_Name, City, and Zip_Code. The company wants to combine those fields into a single column in a specific structured format with the least coding effort. Which approach meets this requirement?

The correct answer is to use AWS Glue DataBrew and the NESTTOMAP transformation. DataBrew is a visual data preparation tool that allows you to clean and normalize data without writing code. The NESTTOMAP transformation is designed to combine multiple columns into a single column of type struct (or map in DataBrew's terminology), which is ideal for structuring address components like DoorNo, StreetName, City, and ZipCode into a single, organized field. NESTTOARRAY would combine the fields into an array, which is an ordered list and not suitable for key-value pairs like address components. PIVOT transforms rows into columns, which is the opposite of the requirement. While a Lambda function could achieve this, it involves writing custom code, which contradicts the "least coding effort" requirement.