GSP622

Overview

Looker Studio allows you to unlock the power of your data with interactive dashboards and beautiful reports that inspire smarter business decisions.

With Looker 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 Looker 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 create data visualizations with Looker Studio. You first explore a sample Google Cloud billing data in 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 export those metrics to Looker Studio, where you explore the service's chief features and build your own billing data visualizations.

What you'll learn

In this lab, you learn how to:

• Explore exported billing data in BigQuery.
• Run SQL queries to better understand a project's consumption of Google Cloud services.
• Export queried data to Looker Studio.
• Explore Looker 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.

Setup and requirements

Before you click the Start Lab button

Read these instructions. Labs are timed and you cannot pause them. The timer, which starts when you click Start Lab, shows how long Google Cloud resources are made available to you.

This hands-on lab lets you do the lab activities in a real cloud environment, not in a simulation or demo environment. It does so by giving you new, temporary credentials you use to sign in and access Google Cloud for the duration of the lab.

To complete this lab, you need:

• Access to a standard internet browser (Chrome browser recommended).

• Time to complete the lab—remember, once you start, you cannot pause a lab.

How to start your lab and sign in to the Google Cloud console

1. Click the Start Lab button. If you need to pay for the lab, a dialog opens for you to select your payment method. On the left is the Lab Details pane with the following:

   • The Open Google Cloud console button
   • Time remaining
   • The temporary credentials that you must use for this lab
   • Other information, if needed, to step through this lab

2. Click Open Google Cloud console (or right-click and select Open Link in Incognito Window if you are running the Chrome browser).

   The lab spins up resources, and then opens another tab that shows the Sign in page.

   Tip: Arrange the tabs in separate windows, side-by-side.

   Note: If you see the Choose an account dialog, click Use Another Account.

3. If necessary, copy the Username below and paste it into the Sign in dialog.

   {{{user_0.username | "Username"}}}

   You can also find the Username in the Lab Details pane.

4. Click Next.

5. Copy the Password below and paste it into the Welcome dialog.

   {{{user_0.password | "Password"}}}

   You can also find the Password in the Lab Details pane.

6. Click Next.

   Important: You must use the credentials the lab provides you. Do not use your Google Cloud account credentials. Note: Using your own Google Cloud account for this lab may incur extra charges.

7. Click through the subsequent pages:

   • Accept the terms and conditions.
   • Do not add recovery options or two-factor authentication (because this is a temporary account).
   • Do not sign up for free trials.

After a few moments, the Google Cloud console opens in this tab.

Note: To access Google Cloud products and services, click the Navigation menu or type the service or product name in the Search field.

Task 1. Explore billing data in BigQuery

In this task, you run SQL queries in BigQuery to see what information is available in the billing data, which was automatically exported into BigQuery when the lab was spun up.

There are four Google Cloud projects identified within this billing data:

• CTG - Storage
• CTG - Dev
• CTG - Prod
• CTG - Sandbox

These four Google Cloud projects illustrate a common enterprise schema, where you have different projects for development, production, storage, and sandbox testing.

1. In the Cloud console, in the Navigation menu (), click BigQuery.

2. In the Query Editor, copy and paste the following query, and then click Run:

SELECT * returns all column values from a specified table.

You should see the resulting table in the Query results section.

Click Check my progress to verify the objective.

Answer the following questions

• Inspect the table (be sure to scroll left and right) and answer the following questions so you become more familiar with this data:

Under the table in Query results, there are 1 million plus rows of data.

You found the answer to this question by looking at the BigQuery table created in your first SQL query. For more complicated questions, you would run more complicated SQL queries to analyze your data and gain valuable insights.

Task 2. Create data visualizations for service type usage with BigQuery and Looker Studio

In this task, you use BigQuery to answer specific questions regarding service types in the billing data.

You want to know which service types are most and least used, so you need to determine:

• What types of services the four projects use
• Which service types are most and least used

To answer these questions, you run SQL queries on the billing data hosted in BigQuery. You then use Looker Studio to build reports with data visualizations to share those insights.

Query to identify service types

1. In the Query editor, clear the current query.

2. In the Query editor, type the following, and then click Run:

This query reveals which service is associated with each log.

The service.description column tells you what Google Cloud service is associated with each log. The GROUP BY keyword aggregates result-set rows that share common criteria (in this case the service description) and returns all of the unique entries found for such criteria.

In the Query results section, in the Results tab, you see that the four projects use 15 different types of Google Cloud services.

Click Check my progress to verify the objective.

Query to find which service types are most and least used

1. In the Query editor, clear the current query.

2. In the Query editor, type the following, and then click Run:

This query determines which service types are most and least used.

The COUNT(*)function returns the number of rows that share the same criteria (in this case the service description).

BigQuery shows your results in the Query results section in a table with two columns: description and num. Compare the numbers in the num column to determine which service type was the most and least used.

Click Check my progress to verify the objective.

Create a Looker Studio visualization for service usage

1. Open Looker Studio in a new tab.

2. Click Create > Report.

3. Select Country and Enter Company name.

4. Agree to the Terms of Service, and then click Continue.

5. In email preferences, select Yes to all (this is connected to your temporary student email).

6. Click Continue.

7. In the Add data to report pane, click Connect to data.

8. In the Google Connectors window, select BigQuery.

9. Click Authorize.

10. Under Recent Projects, select Custom Query.

11. In Billing Project, select your project ID:

12. In the Customer Query pane, type the query you used previously:

13. Click Add.

14. Click Add to report.

15. Click Untitled Report, and rename this report as Services Breakdown.

16. Click Add a Chart, select the Pie chart.

    

    Looker Studio generates a pie chart on the use of services.

17. In the Setup pane, click Blend data.

18. In Blend Data console, in the Metric section, hold the pointer over Record Count, and click X to remove that metric.

19. Click Add metric, and select num.

    You may have to scroll down to see the num menu option.

20. Click Save, then click Close.

21. Click Looker Studio in the top left.

22. To see the visualization you just created, click Regions Breakdown.

23. Close the Looker Studio browser tab and return to the BigQuery console browser tab.

You are now ready to proceed to the next task to answer different questions.

Task 3. Create data visualizations for region usage with BigQuery and Looker Studio

In this task, you use BigQuery to answer specific questions regarding region usage in the billing data.

You want to know which regions are most and least used, so you need to determine:

• Which regions the Google Cloud services ran in
• Which regions are most and least used

To answer these questions, you run SQL queries on the billing data hosted in BigQuery. You then use Looker Studio to build reports with data visualizations to share those insights.

Query to determine which regions the Google Cloud services ran in

1. In the Query editor, clear the current query.

2. In the Query editor, type the following, and then click Run:

The results are a single region column that lists the regions the Google Cloud service ran in. A null region means the region is not known.

Click Check my progress to verify the objective.

Query to determine which regions are used the most and least

1. In the Query editor, clear the current query.

2. In the Query editor, type the following, and then click Run:

Your results are two columns, region and num. Compare the results to determine which regions are most and least used.

Click Check my progress to verify the objective.

Create a Looker Studio visualization for regions

1. Open Looker Studio in a new tab.

2. Click Create > Report.

3. In the Add data to report pane, click Connect to data.

4. In the Google Connectors window, select BigQuery.

5. Under Recent Projects, select Custom Query.

6. In Billing Project, select your project ID:

7. Type the query you used previously:

8. Click Add.

9. Click Add to report.

10. Click Untitled Report, and rename this report to Regions Breakdown.

11. Click Add a Chart, select the Pie chart.

    Looker Studio generates a pie chart on the use of services.

12. In the Setup pane, click Blend data.

13. In Blend Data console, in the Metric section, hold the pointer over Record Count, and click X to remove that metric.

14. Click Add metric, and select num.

    You may have to scroll down to see the num menu option.

15. Click Save, then click Close.

16. Click Looker Studio in the top left.

17. To see the visualizations you just created, click Regions Breakdown.

Congratulations!

In this lab, you explored and queried billing data previously exported to BigQuery. After exploring the data with SQL queries, you exported your aggregated data to Looker Studio, where you generated pie chart visualizations of service and region consumption.

Next steps / Learn more

• Check out the lab titled Examining BigQuery Billing Data in Google Sheets.

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Manual Last Updated February 4, 2026

Lab Last Tested February 4, 2026

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