GSP1043

Overview

A common scenario is where a Google Cloud Data Sharing Partner has proprietary datasets that customers can use for their analytics use cases. Customers need to subscribe to this data, query it within their own platform, then augment it with their own datasets and use their visualization tools for their customer facing dashboards. This enables Data Sharing Partners to simplify and accelerate how they build and deliver value from data-driven solutions.

Through integration with Google Cloud IAM, you can set permissions on BigQuery objects to enable access by users inside or outside of organizations. In this lab, you will learn how Data Sharing Partners create Data Twins for customers either on Google Cloud or a different cloud service provider. For the purposes of this lab, the customer is on Google Cloud in a different project.

In this lab, you will wear two hats and be provided with three Google Cloud projects. In the first project, you are taking on the role of a Data Sharing Partner sharing a dataset generated by the partner's hosted solution using a Data Publishing project. In the second project, you will assume the role of a Data Sharing Partner who will share the source dataset in partner project as an authorized view in the Data Publishing project. In the third project, you will assume the role of a customer who will access the authorized view into their project as a Data Twin and join the data with their solution dataset to create enriched datasets.

Objectives

In this lab, you will:

• Create an authorized view for a Data Sharing Partner Dataset in different projects
• Secure access to the authorized view for a specific customer
• Use a customer generated dataset to join with the Data Sharing Partner dataset to create new insights

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 will be made available to you.

This hands-on lab lets you do the lab activities yourself in a real cloud environment, not in a simulation or demo environment. It does so by giving you new, temporary credentials that 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).

Note: Use an Incognito or private browser window to run this lab. This prevents any conflicts between your personal account and the Student account, which may cause extra charges incurred to your personal account.

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

Note: If you already have your own personal Google Cloud account or project, do not use it for this lab to avoid extra charges to your account.

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 pop-up opens for you to select your payment method. On the left is the Lab Details panel 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 panel.

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 panel.

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 view a menu with a list of Google Cloud products and services, click the Navigation menu at the top-left.

Task 1. Created an authorized table

In the first project, you will take on the role of a Data Sharing Partner creating and sharing a dataset using an authorized table.

Create the Customer Authorized Table

1. From the lab pane. open the Data Sharing Partner Project Console and log in with the associated credentials.

2. From the Navigation Menu, go to BigQuery > BigQuery Studio.

3. Run the following query to create a source dataset by selecting the top 10 cities of each state sorted by land area:

SELECT * FROM ( SELECT *, ROW_NUMBER() OVER (PARTITION BY state_code ORDER BY area_land_meters DESC) AS cities_by_area FROM `bigquery-public-data.geo_us_boundaries.zip_codes`) cities WHERE cities_by_area <= 10 ORDER BY cities.state_code LIMIT 1000;

4. From the toolbar, click More > Query Settings.

5. Select the Set a destination table for query results option.

6. For the Dataset select .demo_dataset.

7. For Table ID type authorized_table.

8. Leave the rest of the fields as default and click Save.

9. Click Run to run the query again to write the results to the table you specified.

10. Verify the authorized_table has been created.

Authorize the dataset

1. From the BigQuery Explorer pane, open the demo_dataset and click + Sharing > Authorize datasets.

2. Add the authorized view that needs to be authorized to share: .demo_dataset.

3. Click Add Authorization.

4. Click Close.

Grant permissions to the Data Publisher to access the view

1. Under your project, inside of demo_dataset, open authorized_table.

2. Click Share.

3. Click on Add Principal and add the Data Publisher and Customer users:

4. Select the BigQuery Data Viewer role.

6. Click Save.

Click Check my progress to verify your performed task.

Created an Authorized Table

Task 2. Create an authorized view in the Data Publishing project

In the second project, you will assume the role of a Data Sharing Partner who will share the source dataset in partner project as an authorized view in the Data Publishing project.

1. Close the Data Sharing Partner Project Console and from the lab pane open the Data Publisher Project Console. Log in with the associated credentials.

2. From the Navigation Menu, go to BigQuery > BigQuery Studio.

3. Run the following query to select cities in New York state from the authorized view:

SELECT * FROM `{{{isv_project.project_id|Data Sharing Partner Project ID}}}.demo_dataset.authorized_table` WHERE state_code="NY" LIMIT 1000

4. On the query toolbar, select Save > Save View.

5. Click in the Dataset field and select data_publisher_dataset.

6. In the Table field, type authorized_view.

7. Click Save. You should now be able to see the dataset and table, as well as query it.

Assign IAM permissions to the Data Publisher view

1. From the BigQuery Explorer pane, open the data_publisher_dataset and click + Sharing > Authorize Views.

2. Add the authorized view that needs to be authorized to share: .data_publisher_dataset.authorized_view.

3. Click Add Authorization.

4. Click Close.

Grant permissions to the customer to access the view

1. Under your project, inside of data_publisher_dataset, open authorized_view.

2. Click Share.

3. Click on Add Principal and add the Customer user:

4. Select the BigQuery Data Viewer role.

6. Click Save.

Click Check my progress to verify your performed task.

Create an authorized view in the Data Publishing project

Task 3. Access the authorized view as a Data Twin

In the third project, the student will assume the role of a customer who will access the authorized view into their project as a Data Twin and join the data with their solution dataset to create enriched datasets.

1. Close the Data Publisher Console and from the lab pane open the Customer (Data Twin) Project Console. Log in with the associated credentials.

2. From the Navigation Menu, go to BigQuery > BigQuery Studio.

3. Execute the following query to access data from the Data Sharing Partner Data publishing project and join the Customer’s data and partner's view to create new insights.

SELECT cities.zip_code, cities.city, cities.state_code, customers.last_name, customers.first_name FROM `{{{consumer_project_2.project_id|Data Twin Project ID}}}.customer_dataset.customer_info` as customers JOIN `{{{consumer_project.project_id|Data Publisher Project ID}}}.data_publisher_dataset.authorized_view` as cities ON cities.state_code = customers.state;

Your results should resemble the following:

4. On the query toolbar, select Save > Save View.

5. Click in the Dataset field and select customer_dataset.

6. In the Table field, type customer_table.

7. Click Save. You should now be able to see the dataset and table, as well as query it.

Click Check my progress to verify your performed task.

Access the authorized view as a Data Twin

Task 4. Confirm functionality of the Customer (Data Twin)

To confirm the functionality of the Data Twin, you will insert a new row in the Data Sharing Partner Project and test the functionality inside of the customer project.

1. Close the Customer (Data Twin) Project Console and from the lab pane open the Data Sharing Partner Project Console. Log in with the associated credentials.

2. From the Navigation Menu, go to BigQuery > BigQuery Studio.

3. Run the following query to insert a new row in the Data Sharing Partner dataset:

INSERT INTO `{{{isv_project.project_id|Data Sharing Partner Project ID}}}.demo_dataset.authorized_table` (zip_code, city, county, state_fips_code, state_code, state_name, fips_class_code, mtfcc_feature_class_code, functional_status, area_land_meters, area_water_meters, cities_by_area) VALUES ("11012", "New City", "New County", "02", "NY", "New York", "B5", "G6350", "S", 123632007174.0, 544474039.0, 10)

You should see the following output

This statement added 1 row to authorized_table.

4. Close the Data Sharing Partner Project Console and from the lab pane open the Customer (Data Twin) Project Console. Log in with the associated credentials.

5. Finally, query the view in Customer (Data Twin) project to confirm the newly added row is visible.

SELECT cities.zip_code, cities.city, cities.state_code, customers.last_name, customers.first_name FROM `{{{consumer_project_2.project_id|Data Twin Project ID}}}.customer_dataset.customer_info` as customers JOIN `{{{consumer_project.project_id|Data Publisher Project ID}}}.data_publisher_dataset.authorized_view` as cities ON cities.state_code = customers.state;

You results should resemble the following:

Congratulations!

In this lab, you created an authorized view for an Data Sharing Partner dataset to share with a user in a Data Publishing project. You then logged into the Data Publishing project and shared the authorized view with a customer user in a Data Twin project. Lastly, you logged into the Customer/Data Twin project and joined the data with customer specific data to create an enriched dataset.

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Manual Last Updated March 18, 2024

Lab Last Tested March 18, 2024

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