Service Cost Estimation for Google Cloud Platform

D.

The best way to analyze Google Cloud Platform service costs from multiple projects is to export your billing data to a BigQuery dataset, and then write time window-based SQL queries for analysis. Therefore, option D is the correct answer.

BigQuery is a powerful, fully managed, cloud-native data warehouse that allows you to store, analyze, and query large datasets quickly and efficiently. By exporting your billing data to a BigQuery dataset, you can easily analyze your costs across multiple projects and services using SQL queries.

To export your billing data to a BigQuery dataset, you can follow these steps:

1. Open the Google Cloud Console and navigate to the Billing section.
2. Select the project you want to export the billing data from.
3. Click on the "Billing export" menu item and then click on the "Create a new export" button.
4. Choose "BigQuery" as the export destination, and select or create a BigQuery dataset.
5. Choose the data range and frequency for the export, and configure any additional options as needed.
6. Save the export configuration and start the export.

Once the billing data is exported to the BigQuery dataset, you can use SQL queries to analyze the data and generate service cost estimates by service type, daily and monthly, for the next six months. You can use time window-based SQL queries to group the billing data by service type, day, and month, and calculate the costs using aggregation functions.

Option A is incorrect because Cloud Bigtable is a NoSQL database, which is not suitable for analyzing billing data. Option B is also incorrect because Google Sheets is not suitable for analyzing large datasets and does not support SQL queries. Option C is also not recommended because it requires downloading and processing the billing data locally, which can be time-consuming and error-prone.