Overview

In Google Cloud, permissions are not assigned directly to users, groups, or service accounts. Instead, users, groups, or service accounts are granted access to basic, predefined, or custom Identity and Access Management (IAM) roles to give them permissions to perform actions on specific Google Cloud resources such as BigQuery. There are many predefined IAM roles for BigQuery that can be granted at various levels of the resource hierarchy including at the Google Cloud project level and the BigQuery dataset level.

A predefined IAM role can provide different permissions depending on which level of the Google Cloud hierarchy it is applied. For example, when applied at the BigQuery dataset level, the role of BigQuery data editor provides the ability to create, update, and delete the tables in the dataset. When the same role is applied at the Google Cloud project level, it provides the ability to create new BigQuery datasets in the project. Depending on your organization's needs and requirements, you can choose which roles to apply at which level of the Google Cloud hierarchy for any individual user, group, or service account to control access to BigQuery resources.

In this lab, you work in a simulated enterprise environment that has two Google Cloud projects and two users: one user with full access (project owner) on Project 1 and another user with limited access (project viewer) in Project 2. Using the two Google Cloud projects, you explore and test access to the Google Cloud projects and grant access to specific BigQuery datasets using predefined IAM roles.

Objectives

In this lab, you learn how to:

• Explore and test user access to Google Cloud projects and BigQuery data.
• Create a BigQuery dataset and table.
• Share a BigQuery dataset using predefined IAM roles.

Setup and requirements

Note: It can take 3 - 5 minutes for the lab environment to auto-generate two Google Cloud Projects and two user accounts.

Note: Once started, the lab cannot be paused, and ending the lab will delete all student projects that are running.

For each lab, you get a new Google Cloud project and set of resources for a fixed time at no cost.

1. Sign in to Google Skills using an incognito window.

2. Note the lab's access time (for example, 1:15:00), and make sure you can finish within that time. There is no pause feature. You can restart if needed, but you have to start at the beginning.

3. When ready, click Start lab.

4. Note your lab credentials (Username and Password). You will use them to sign in to the Google Cloud Console.

5. Click Open Google Console.

6. Click Use another account and copy/paste credentials for this lab into the prompts. If you use other credentials, you'll receive errors or incur charges.

7. Accept the terms and skip the recovery resource page.

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 right is the Lab setup and access panel with the following:

   • The Open Google Cloud console button
   • The temporary credentials (username and password) that you must use for this lab
   • Other information, if needed, to step through this lab

   Note that the lab timer is located near the top of the page, showing the remaining time.

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

Task 1. Confirm User 1 access to the Project 1

In this task, you work in Project 1 () to explore the permissions of User 1 in Project 1. Be sure that you log in to this project using the credentials for User 1 ().

1. In a new Incognito window, click Open Google Console for Project 1 (), and log in using the credentials for User 1 ().

2. In the Google Cloud console, in the Navigation menu (), click IAM & Admin > IAM.

The IAM page displays permissions at the project level for all principals (users). As a project owner, User 1 can access and modify IAM permissions in Project 1.

Note: As User 1, you are a project owner of Project 1. Check that you see the Owner role.

3. At the top of the Google Cloud console, click on the Select a Project dropdown menu to see a list of projects that User 1 is logged into.

The drop-down menu will show the project ID for Project 1 in the drop-down menu: .

Task 2. Create a BigQuery dataset and table in Project 1

In this task, you continue to work in Project 1 () as User 1 to create a new BigQuery dataset and table. Be sure that you remain logged into this project using the credentials for User 1 ().

Create a dataset

1. In the Google Cloud console, in the Navigation menu (), under Analytics, click BigQuery. When prompted click Done.

2. In the Explorer pane (left side pane), click View Action (three vertical dots) next to your Project ID, and select Create dataset.

3. For Dataset ID, type: wikipedia_data

4. For Location type, select Multi-region and US (multiple regions in United States).

5. Click Create dataset.

Create a table

1. In the Explorer pane, click View Action (three vertical dots) next to wikipedia_data, and select Create table.

2. For Create table from, select Google Cloud Storage.

3. For Select file from GCS bucket, type: tcd_repo/data/entertainment_media/wikipedia_benchmark/csv/Wiki1B-*.csv

The file format will update to CSV.

Note: Notice the asterisk (`*`) in the file pattern. There is approximately a gigabyte (GB) of data that will be imported from multiple CSV files into the new table.

4. For Project, leave the default value ().

5. For Dataset, leave the default value (wikipedia_data).

6. For Table, type: wiki_table_csv

7. For Table type, leave the default value (Native table).

8. In the Schema section, enable the Auto detect checkbox.

9. Expand the Advanced options section.

10. For Header rows to skip, type 1

11. Click Create Table.

Run a query on the loaded data

1. In the query editor, copy and paste the following query, and click Run.

SELECT Title, COUNT(views) AS views FROM wikipedia_data.wiki_table_csv WHERE CONTAINS_SUBSTR(title, 'GOOGLE') GROUP BY Title ORDER BY views DESC LIMIT 100;

Using the newly populated data, this query counts the number of views for Wikipedia articles with "GOOGLE" in the title.

Click Check my progress to verify the objective. Create BigQuery dataset and table

Sign out of Project 1

1. Click on the profile icon on the top right of the Google Cloud console.

2. Click Sign out.

If asked to confirm, click Leave.

Task 3. Log into Project 2 as User 2 to confirm restricted project access

In this task, you work in Project 2 () to explore the permissions for User 2 in Project 2. Be sure that you log in to this project using the credentials for User 2 ().

1. In a new Incognito window, click Open Google Console for Project 2 (), and log in using the credentials for User 2 ().

2. At the top of the Google Cloud console, click on the Select a Project dropdown menu to see a list of projects that User 2 is logged into.

The drop-down menu will show the project ID for Project 2 in the drop-down menu: .

3. In the Google Cloud console, in the Navigation menu (), click IAM & Admin > IAM.

User 2 () is a project viewer in Project 2. As a project viewer in Project 2, User 2 can run query jobs on BigQuery data stored in Project 2 or in any other project as long as the user has been given access to the data in the other project.

Task 4. Run a query in Project 2 to confirm restricted data access

In this task, you continue to work in Project 2 () as User 2 () to test your access to the BigQuery data in Project 1. Be sure that you remain logged into this project using the credentials for User 2 ().

1. In the Google Cloud console, in the Navigation menu (), under Analytics, click BigQuery. When prompted click Done.

2. In the query editor, run the following query, replacing ProjectID with the Project 1 ID () to specify the project that hosts the data:

SELECT Title, COUNT(views) AS views FROM `ProjectID.wikipedia_data.wiki_table_csv` WHERE CONTAINS_SUBSTR(title, 'GOOGLE') GROUP BY Title ORDER BY views DESC LIMIT 100; Note: In BigQuery, even if a project is not pinned or visible in your Explorer pane, you can still query BigQuery data if you have been granted access to that data.

In this case, User 2 is not able to query the data created by User 1 in Project 1 because User 2 has not been granted access to the data. The error message states that access to the data is denied.

Sign out of project 2

1. Click on the profile icon on the top right of the Google Cloud console.

2. Click Sign out.

If asked to confirm, click Leave.

Task 5. Grant access to User 2 on the Project 1 data

You can assign BigQuery roles to users that do not have access to the Google Cloud project that hosts the data. This flexibility makes it easy to control and customize the level of access provided to any user, group, or service account because users can run queries in their own projects on only the specific data that they have been granted access to, even if that is hosted in other Google Cloud projects.

In this task, you return to work in Project 1 () to grant access to User 2 on the Project 1 dataset. Be sure that you log in to this project using the credentials for User 1 ().

Using the Google Cloud console, you assign the BigQuery data editor role on the dataset named wikipedia_data in Project 1 to User 2.

1. In a new Incognito window, click Open Google Console for Project 1 (), and log in using the credentials for User 1 ().

2. In the Google Cloud console, in the Navigation menu (), under Analytics, click BigQuery.

3. In the Explorer pane, click View Action (three vertical dots) next to wikipedia_data, and select Share > Manage Permissions.

4. Click Add principal.

5. For New principals, enter the email for User 2:

6. For Select a role, select BigQuery Data Editor under BigQuery.

7. Click Save.

Click Check my progress to verify the objective. Assign BigQuery data editor role to User 2

Sign out of project 1

1. Click on the profile icon on the top right of the Google Cloud console.

2. Click Sign out.

If asked to confirm, click Leave.

Task 6. Run query as User 2 in Project 2 to confirm access

For this task, you return to work in Project 2 () to test the data access that was granted to User 2. Be sure that you log in to this project using the credentials for User 2 ().

1. In a new Incognito window, click Open Google Console for Project 2 (), and log in using the credentials for User 2 ().

2. At the top of the Google Cloud console, click on the Select a Project dropdown menu to see a list of projects that User 2 is logged into.

Notice that as User 2, you can still only see Project 2 because you were not granted access to Project 1. User 2 was only granted access to the BigQuery dataset in Project 1, not the actual project.

3. In the query editor, run the following query, replacing ProjectID with the Project 1 ID () to specify the project that hosts the data:

SELECT Title, COUNT(views) AS views FROM `ProjectID.wikipedia_data.wiki_table_csv` WHERE CONTAINS_SUBSTR(title, 'GOOGLE') GROUP BY Title ORDER BY views DESC LIMIT 100;

As User 2, you can now successfully run the query on the BigQuery dataset in Project 1. Assigning the role of BigQuery data editor on the BigQuery dataset allows User 2 to run queries on the tables in the BigQuery dataset (and more actions including creating, modifying, and deleting tables), even though User 2 does not have direct access to Project 1.

Click Check my progress to verify the objective. Run query on Project 1 data as User 2 in Project 2

End your lab

When you have completed your lab, click End Lab. Google Skills removes the resources you’ve used and cleans the account for you.

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