One more brick: Dynamic Views

In the realm of data management, especially in environments where a consumption layer is accessible to end-users or various systems, security becomes a important aspect. Databricks offers effective solutions for this purpose, enabling the implementation of security measures at both the row and column level, as well as the use of data masking.

A powerful tool in this context is the ability to use Unity Catalog to centralize data access policies. This not only simplifies security management but also provides us with advanced functionalities, such as the predefined functions current_user(), is_account_group_member(<group name>) and is_member(<group name>) . It's crucial, when granting access to a specific table, to protect the integrity and security of the underlying data by avoiding direct access to the source tables of dynamic views.

Column-Level Permissions

With dynamic views, we can limit access for certain users and protect or impose additional restrictions on a specific set of columns.

All users with read and access permissions on sales_redacted can view its content, but only members of the 'auditors' group can see the content of the 'email' column.

Row-Level Permissions

In addition to filtering access at the column level, it is possible to limit access to the complete dataset by using the mentioned functions as predicates in queries.

Here, only members of the 'managers' group will see all records, while other users will have limited access.

Data Masking

All security functionalities are accessible through Spark SQL. Data masking, using functions like regex and replace, allows hiding data from end clients.

Managing permissions is key to ensuring that security and access policies are effective.

• GRANT SELECT ON VIEW sales_redacted TO 'auditors';
• GRANT USAGE ON VIEW sales_redacted TO 'auditors';
• ALTER GROUP auditors ADD USER '[email protected]';
• ALTER GROUP auditors ADD GROUP 'other_auditors_group';

These commands allow for effective administration of security in Databricks.

Data access rules are priority in any data management environment, and Databricks offers a robust set of tools to ensure it. By using permissions at the column and row levels, along with data masking techniques and proper permission management, we can efficiently protect our information while maintaining the necessary flexibility and accessibility for our users. The key lies in understanding and correctly applying these tools to create a secure and functional environment.

Ref: Databricks Permissions