Visualizing Billing Data with Data Studio
Google Data Studio allows you to unlock the power of your data with interactive dashboards and beautiful reports that inspire smarter business decisions.
With Data Studio, you can:
- Connect: easily access a wide variety of data. With built in and partner connectors, you can connect to virtually any type of data stream.
- Visualize: turn your data into compelling stories of data visualization art. You can quickly build dashboards with Data Studio's web-based reporting tools.
- Share: share your reports and dashboards with individuals, teams, or the world. Collaborate in real time. Embed your report on any page.
In this lab, you will learn how to build data visualizations with Google Data Studio. You will first explore a sample GCP bill and learn how to export the billing data to BigQuery—Google's serverless, highly scalable enterprise data warehouse that is designed to make data analysts more productive with unmatched price-performance.
After running a few SQL queries on your billing data, you will export those metrics to Data Studio, where you will explore the service's chief features and build your own billing data visualizations.
In this lab, you will learn how to:
- Use the billing service in the GCP Console to explore projects and their consumption of cloud computing resources.
- Export billing data to BigQuery.
- Explore your billing data in BigQuery.
- Run SQL queries to better understand a project's consumption of GCP services.
- Export your queried data to Data Studio.
- Explore Data Studio tools and generate visualizations with your queried data.
Once you're ready, scroll down and follow the steps below to get your lab environment set up.
加入 Qwiklabs 即可阅读本实验的剩余内容…以及更多精彩内容！
- 获取对“Google Cloud Console”的临时访问权限。
- 200 多项实验，从入门级实验到高级实验，应有尽有。
Explore your billing data in BigQuery
Run the query to get service.description column values
Run a query to find out which services are used the most and least
Run the query to get the region of the GCP service ran
Run the query to find out which regions are used the most and the least by a service