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You receive data files in CSV format monthly from a third party.

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You receive data files in CSV format monthly from a third party. You need to cleanse this data, but every third month the schema of the files changes. Your requirements for implementing these transformations include:

    Executing the transformations on a schedule

    Enabling non-developer analysts to modify transformations

    Providing a graphical tool for designing transformations

What should you do?

A.

Use Cloud Dataprep to build and maintain the transformation recipes, and execute them on a scheduled basis

B.

Load each month’s CSV data into BigQuery, and write a SQL query to transform the data to a standard schema. Merge the transformed tables together with a SQL query

C.

Help the analysts write a Cloud Dataflow pipeline in Python to perform the transformation. The Python code should be stored in a revision control system and modified as the incoming data’s schema changes

D.

Use Apache Spark on Cloud Dataproc to infer the schema of the CSV file before creating a Dataframe. Then implement the transformations in Spark SQL before writing the data out to Cloud Storage and loading into BigQuery

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