DEA-C01 · Question #39
A company receives .csv files that contain physical address data. The data is in columns that have the following names: Door_No, Street_Name, City, and Zip_Code. The company wants to create a single c
The correct answer is B. Use AWS Glue DataBrew to read the files. Use the NEST_TO_MAP transformation to create the. To combine multiple columns of physical address data into a single, structured column in a map format with the least coding effort, AWS Glue DataBrew with the NEST_TO_MAP transformation is the most suitable solution.
Question
A company receives .csv files that contain physical address data. The data is in columns that have the following names: Door_No, Street_Name, City, and Zip_Code. The company wants to create a single column to store these values in the following format:
Which solution will meet this requirement with the LEAST coding effort?
Options
- AUse AWS Glue DataBrew to read the files. Use the NEST_TO_ARRAY transformation to create
- BUse AWS Glue DataBrew to read the files. Use the NEST_TO_MAP transformation to create the
- CUse AWS Glue DataBrew to read the files. Use the PIVOT transformation to create the new
- DWrite a Lambda function in Python to read the files. Use the Python data dictionary type to create
How the community answered
(42 responses)- A7% (3)
- B86% (36)
- C2% (1)
- D5% (2)
Why each option
To combine multiple columns of physical address data into a single, structured column in a map format with the least coding effort, AWS Glue DataBrew with the NEST_TO_MAP transformation is the most suitable solution.
NEST_TO_ARRAY would create an array of values (e.g., ['123', 'Main St', ...]), not a key-value map as specified by the desired output format.
The NEST_TO_MAP transformation in AWS Glue DataBrew specifically creates a map (key-value pairs) from selected columns, directly matching the desired JSON-like output format of {'Key': 'Value', ...}. This is a low-code/no-code operation within DataBrew, fulfilling the 'least coding effort' requirement for data restructuring.
PIVOT transformation typically reorients data from rows to columns or aggregates it, which is not the objective of combining multiple columns into a single structured column.
Writing a Lambda function in Python requires coding, which goes against the requirement for the 'LEAST coding effort' when DataBrew offers a visual, managed transformation.
Concept tested: AWS Glue DataBrew data restructuring transformations
Source: https://docs.aws.amazon.com/databrew/latest/dg/nest-array-map.html
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