Data Masking for Apache Hive
id | masked_ssn | name |
---|---|---|
1 | XXX-6789 | Alice |
2 | XXX-4321 | Bob |
Advantages of view-based masking:
- Simple implementation with SQL.
- No additional tools required.
- Provides column-level data protection.
2. Data Virtualization Approach for RLS in Hive
Since Hive doesn’t support row-level security (RLS) natively, a data virtualization workaround can be used to achieve a similar result by redirecting queries to masked views.
How It Works
- Restrict access to the original table.
- Create a masked view in a user-specific schema.
- Set the user's default schema to automatically query the masked view.
Example: Masking SSNs for Analyst
CREATE DATABASE analyst1_db;
CREATE VIEW analyst1_db.users AS
SELECT id, CONCAT('XXX-', SUBSTR(ssn, -4)) AS ssn, name
FROM default.users;
Expected Output:
When the analyst runs:
SELECT * FROM users;
They’ll query the masked view (analyst1_db.users
), ensuring data protection.
Expected Query Results
Query Executed | Accessed Table | Result (Masked/Unmasked) |
---|---|---|
SELECT * FROM users; (Analyst) | analyst1_db.users | Masked (XXX-6789) |
SELECT * FROM users; (Admin) | default.users | Unmasked (123-45-6789) |
This data virtualization technique offers a practical workaround for Hive but isn't a perfect substitute for row-level security. It may add complexity with user-specific schemas and could cause confusion if not documented properly. For a more robust solution, consider integrating Apache Ranger or other dedicated tools.
3. Data Masking for Apache Hive with Apache Ranger
Apache Ranger offers centralized access control with fine-grained masking capabilities. Ranger allows for:
- Static masking: Fixed transformations such as replacing values with nulls or constants.
- Dynamic masking: User-role-based transformations where sensitive data visibility depends on permissions.
Example: Applying a Masking Policy in Apache Ranger
- Define a data masking policy in Ranger for the
users
table. - Set column-level masking rules for the
ssn
column. - Assign roles to control which users see masked vs. unmasked values.

Query Results for Ranger Policy Example:
User | Column | Query Result |
---|---|---|
Analyst | ssn | Mask with NULL |
Guest | ssn | Mask with NULL |
Administrator | ssn | Unmasked |
Data Masking for Apache Hive Using DataSunrise
1. Connect Your Hive Instance to DataSunrise
Once DataSunrise is installed, configure it to connect to your Hive environment by specifying connection parameters.

2. Define Masking Rules
Create data masking rules in DataSunrise to specify which columns need to be masked and the masking methods to apply. DataSunrise supports both dynamic and static data masking capabilities, each configurable within their respective UI sections. For this demonstration, we focus on dynamic masking, specifying the exact data to be masked.

3. Test and Validate
Run queries to verify that data masking is applied correctly without impacting query performance.

Conclusion
Data masking is essential for securing sensitive data in Apache Hive and ensuring regulatory compliance. While Hive views and data virtualization offer basic masking capabilities, they often require manual configuration and lack flexibility. Apache Ranger provides centralized control but can be complex to manage and configure effectively.
DataSunrise provides a superior solution, delivering dynamic and static data masking with minimal performance impact. Its intuitive interface, flexible policies, and seamless Hive integration make it the ideal and scalable choice for enhancing data security.
DataSunrise offers advanced database security features, including auditing, masking, and data discovery. Schedule an online demo to see how we can help secure your Hive-stored data.