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Answer: Do daily exports of Cloud Logging data to BigQuery. Create views filtering by project, log type, resource, and user.
The correct answer is A. Daily exporting of Cloud Logging data to BigQuery and creating views filtering by project, log type, resource, and user is the most efficient and scalable approach. This method leverages BigQuery's powerful querying capabilities and advanced analysis features, automates the data export process, and allows for tailored reports through customized views. Such an approach ensures consistent and efficient data transfer and simplifies repeated analysis by encapsulating complex SQL queries in views.
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As a Google Professional Data Engineer, you have a new customer who wants to keep track of their Google Cloud compute resource usage in a detailed manner. The customer has specifically requested daily reports that not only show the net consumption of these resources but also identify the users who utilized them. Your task is to quickly and efficiently produce these daily reports. What approach should you take to fulfill this requirement?
A
Do daily exports of Cloud Logging data to BigQuery. Create views filtering by project, log type, resource, and user.
B
Filter data in Cloud Logging by project, resource, and user; then export the data in CSV format.
C
Filter data in Cloud Logging by project, log type, resource, and user, then import the data into BigQuery.
D
Export Cloud Logging data to Cloud Storage in CSV format. Cleanse the data using Dataprep, filtering by project, resource, and user.
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