When encountering access denied errors in BigQuery, understanding the proper permission request process is essential for data professionals. Learn about IAM roles, dataset-level access control, and the correct procedures to gain access to restricted datasets.
Table of Contents
Question
While running a query on BigQuery you tried to get a dataset you did not have access to and got an accessDenied 403 error. What should you do to fix this?
A. Contact your manager and ask for access to the dataset
B. Go to your BigQuery configuration and give yourself access to the dataset
C. Change the dataset status from Read-only to Read-Write
D. Contact the resource owner and ask for access to the dataset
Answer
D. Contact the resource owner and ask for access to the dataset
Explanation
Understanding BigQuery Access Denied Errors
When you receive a 403 Access Denied error in BigQuery with a message like “Permission denied on dataset (or it may not exist).(accessDenied)”, it means your Google account lacks the necessary permissions to access the specified dataset1. This is a security measure implemented through Google Cloud’s Identity and Access Management (IAM) system.
Why Contacting the Resource Owner is the Correct Solution
BigQuery uses a hierarchical permission structure where access is controlled at multiple levels:
- Project level: Permissions granted at this level are inherited by all datasets within the project
- Dataset level: More granular permissions can be applied specifically to individual datasets
- Table/view level: Even more specific access controls can be implemented
Only users with administrative privileges or ownership roles for the dataset can modify these permissions. As a regular user encountering an access denied error, you cannot:
- Give yourself access to the dataset (eliminating option B)
- Change a dataset’s status from “Read-only” to “Read-Write” as this isn’t a standard BigQuery setting (eliminating option C)
Why Other Options Are Incorrect
Option A: While contacting your manager might be part of your organization’s internal process, from a technical perspective, only the resource owner or someone with administrative privileges for the dataset can grant you access.
Option B: Regular users cannot grant themselves access to datasets they don’t already have permissions for. The BigQuery UI will not show permission options for resources you don’t have administrative access to.
Option C: There is no standard setting to change a dataset from “Read-only” to “Read-Write” in BigQuery. Access is managed through specific IAM roles like bigquery.dataViewer or bigquery.dataEditor.
How Permissions Work in BigQuery
When the resource owner receives your request, they can grant you access by:
- Navigating to the BigQuery section in Google Cloud Console
- Locating the specific dataset
- Accessing the sharing or permissions settings
- Adding your account with appropriate roles such as:
- bigquery.dataViewer (for read access)
- bigquery.dataEditor (for write access)
The resource owner might also implement column-level access control for sensitive data, allowing you to see only specific columns within tables.
In conclusion, the proper procedure when encountering a 403 Access Denied error in BigQuery is to contact the resource owner and request the necessary permissions to access the dataset.
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