Restrict other users to perform any actions on my BigQuery tables

[vigneswar17]

Hi Folks,

There are risks that user overwrites an existing BigQuery table. Let's say a user wants to create a table within a dataset and ideally that user would only be able to overwrite their own tables. How can we implement this table owner restriction during table creation itself? Do we have any solution in BigQuery or any workarounds for this?

Are there any way to submit code via SAS to BQ to unify or extend the expiration date for a list of tables within a dataset with the same 'session' suffix, that might give the user more confidence and control over 'session' management.  Is there any solution apart from doing this within the console one table at a time.

Any leads on this are much appreciated!

Thanks,

Vigneswar 

[ms4446]

Preventing users from overwriting existing BigQuery tables requires a combination of approaches involving permissions, best practices, and potentially some scripting/automation.

Key Strategies:

1. Granular IAM Permissions

• Principle of Least Privilege: Grant users the minimum permissions needed for their roles. Avoid broad project-wide or dataset-level write/edit permissions, focusing instead on more specific, table-level permissions.
• Custom Roles: Create Custom Roles for fine-grained control, allowing actions like bigquery.tables.create and bigquery.tables.updateData. Test these roles to ensure they function as intended in your environment.
• Effective Permissions Check: Utilize the "Check Effective Permissions" tool in the Cloud Console to confirm the intended access level.

2. Naming Conventions and Table Ownership Metadata

• Clear Naming: Implement naming conventions (e.g., userA_table1) to indicate table ownership.
• Ownership Metadata: Maintain a metadata table in BigQuery with details like table name, owner, and creation date. Automate updates to this table using scripts or Cloud Functions for efficiency.

3. Enforce Best Practices through Education and Processes

• User Education: Train users on the importance of avoiding overwrites and adhering to established protocols.
• Review Processes: Set up review or approval processes for critical table changes, involving data owners or designated teams.
• Regular Audits: Conduct audits and monitor table accesses and modifications to complement these measures.

4. Automation for Expiration Management

• Scripting in Compatible Languages: Develop scripts in languages like Python, which have BigQuery client libraries, to manage table expirations. Note that SAS might not directly interface with BigQuery, so consider using Python or other compatible languages for this purpose.
• SAS Integration: If SAS is integral to your workflow, explore intermediary steps or tools that enable SAS to interact with BigQuery, such as data export/import or connectors/APIs.

Key Considerations and Workarounds:

• No Native Ownership Enforcement: Combine IAM, metadata management, versioning, backups, and the use of temporary tables or views, as BigQuery lacks built-in ownership enforcement.
• Logging and Alerting: Implement mechanisms for logging and alerting on table operations for quick identification and response to unauthorized actions.

Example Implementation:

• IAM Setup: Create a custom role with permissions like bigquery.tables.create and bigquery.tables.updateData. Grant this role at the dataset level and remove broader permissions.
• Naming Convention: Instruct users to use specific prefixes for their tables (e.g., userA_tablename).
• Ownership Metadata: Establish a BigQuery table for storing ownership information, updated automatically via scripts or Cloud Functions.

[vigneswar17]

Thanks for your response.

As mentioned expectation is ownership needs to be implemented for tables through query itself while creating the table. Is there any way to handle it ?

[ms4446]

Unfortunately, you cannot directly enforce true table ownership through the query itself when creating a table in BigQuery. There's no built-in mechanism within the SQL syntax to assign and restrict table modification permissions during the CREATE TABLE statement. 

Here are some strategies to mitigate the issue:

Key Points:

1. IAM as Primary Control: BigQuery primarily uses Identity and Access Management (IAM) for permission handling. Users require appropriate IAM roles to create and modify tables within a dataset. This is the primary method for controlling access.

2. Metadata, Not Enforcement: While you can add "owner" metadata during table creation, this alone doesn't prevent someone with broader permissions from overwriting the table. Metadata serves for informational purposes rather than active enforcement.

Mitigation Strategies:

1. Granular Permissions and User-specific Views:

   • Custom IAM Roles: Create roles that allow users to execute queries and create tables, but restrict data updates to tables where they meet specific metadata conditions (e.g., an "owner" column).
   • User-specific Views: Implement views filtering tables based on "owner" metadata. This adds a layer of protection, though it may not be foolproof for users with broader dataset permissions.

2. Templated Queries and Stored Procedures:

   • Query Templates: Provide templates that include user identifiers in an "owner" column, relying on user adherence.
   • Stored Procedures: Use stored procedures to encapsulate table creation logic, enforce ownership tracking, and check permissions.

3. External Workflow Management Tools:

   • Implement tools or systems for data orchestration to centrally manage table creation and modification, enforcing ownership and permission rules.

Additional Considerations:

• Audit and Monitoring: Implement audit logging and monitoring to track and address unauthorized changes.
• Data Governance Policies: Establish and enforce clear data governance policies, educating users on responsible data handling.
• Regular Policy Review: Continuously review and update IAM policies, stored procedures, and templates to align with changing security needs and organizational policies.
• Testing and Validation: Thoroughly test custom IAM roles and stored procedures to ensure they function as intended without disrupting operations.

While BigQuery doesn't support native query-level ownership enforcement, a combination of IAM roles, metadata management, templated queries, stored procedures, and external workflow tools can provide a structured approach to managing table access and modifications. It's crucial to support these technical measures with strong data governance, regular policy reviews, and user education to maintain data security and integrity in BigQuery.

---