By Technology
Mage Data for BigQuery
Extend BigQuery security with automated sensitive-data discovery, centralized policy management and dynamic masking across cloud workloads.
How Mage Data Helps
As data flows into BigQuery from multiple sources, it often carries sensitive information -- PII, financial data, healthcare records, and more. Mage Data connects directly to BigQuery to discover and classify all sensitive data across datasets and tables.
Apply masking policies to create sanitized views or copies of BigQuery data for analytics, development, and sharing with third parties -- all while maintaining compliance with GDPR, HIPAA, PCI-DSS, and other regulations.
Key Challenges We Solve
- Native BigQuery connector for seamless integration
- Automated discovery across all datasets and tables
- In-place masking without data movement
- Policy-based masking for multi-team access control
Key Capabilities
Mage Data for BigQuery Overview
Automated Discovery
Uncover and classify sensitive information across all BigQuery datasets and projects with minimal manual effort.
Advanced Masking Techniques
Choose from a wide range of sophisticated masking algorithms to protect data without losing analytical value.
Simplified Policy Management
Manage all data protection rules for BigQuery and other platforms from a single, centralized console.
Cross Platform Flexibility
Apply consistent security standards to data moving between BigQuery and on-premises or other cloud databases.
Seamless Integration with BigQuery DDM
Enhance BigQuery's native dynamic masking with additional enterprise-grade security and governance controls.
Frequently Asked Questions
Mage Data scans table schemas and column contents and classifies fields such as names, national identifiers, card numbers and health data. Mage Data can also inherit classifications Google has already made in its Knowledge Catalog, so nothing is scanned twice. The result is an inventory of which datasets and columns hold regulated data — the foundation every masking rule and access policy is built on.
Static data masking transforms the data once, when it is written or copied, so queries run against ordinary columns with zero runtime overhead. Dynamic data masking applies at query time instead, leaving stored data unchanged with modest per-query processing. Mage Data supports both, so you choose per dataset: static for non-production copies, dynamic where real data must remain.
Copies are where most exposure happens, because warehouse controls do not travel with an extract. Mage Data closes that gap three ways: mask in place, so the copy is taken from data that is already protected; mask files as they are exported, with file watchdogs detecting each extract landing in Cloud Storage; or use secure data pipelines to extract, mask and load in one step, so real values are transformed in transit and never written to the destination.
Yes. Mage Data's dynamic data masking applies rules at query time based on attributes such as user or role, so the same query returns full values to one user and masked values to another — with no change to the stored data and no rewriting of the application.
Secure Your BigQuery Data
See how Mage Data integrates natively with Google BigQuery to discover, classify, and protect sensitive data in your cloud data warehouse.