You need to create a weekly aggregated sales report based on a large volume of data. You want to use Python to design an efficient process for generating this report. What should you do?
A.
Create a Cloud Run function that uses NumPy. Use Cloud Scheduler to schedule the function to run once a week.
B.
Create a Colab Enterprise notebook and use the bigframes.pandas library. Schedule the notebook to execute once a week.
C.
Create a Cloud Data Fusion and Wrangler flow. Schedule the flow to run once a week.
D.
Create a Dataflow directed acyclic graph (DAG) coded in Python. Use Cloud Scheduler to schedule the code to run once a week.
Using Dataflow with a Python-coded Directed Acyclic Graph (DAG) is the most efficient solution for generating a weekly aggregated sales report based on a large volume of data. Dataflow is optimized for large-scale data processing and can handle aggregation efficiently. Python allows you to customize the pipeline logic, and Cloud Scheduler enables you to automate the process to run weekly. This approach ensures scalability, efficiency, and the ability to process large datasets in a cost-effective manner.
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