How do I mask sensitive data like emails in PostgreSQL with Greenmask?

Use built-in transformers such as 'hash' or 'random' with dynamic parameters. Configure in YAML to target specific columns, ensuring PII is anonymized while preserving data formats for testing.

Greenmask vs pg_dump: which one should I use?

Choose Greenmask if you need data masking, subsetting, or synthetic generation beyond simple backups. For basic backup without transformation, pg_dump is lighter and more straightforward.

Does Greenmask fully support MySQL yet?

MySQL support is currently in beta and labeled as work-in-progress, so it's functional but not production-ready. Check the GitHub issues for ongoing updates and stability.

How to create a subset database for local development with Greenmask?

Define subsetting rules in the configuration file to filter rows while maintaining referential integrity. Use the CLI to dump a smaller, intact database that mirrors production relationships.

Can Greenmask generate synthetic test data from scratch?

Yes, via the CMD transformer or custom scripts, it can generate realistic mock data. This is useful for populating empty environments with test data that mimics production logic.

What storage options does Greenmask support for backups?

It supports local directories and S3-compatible storage like AWS S3, MinIO, Google Cloud Storage, and Azure Blob Storage, offering versatility for both cloud and on-premise setups.

Open-Awesome

Greenmask

Apache-2.0Gov0.2.21

An open-source tool for PostgreSQL and MySQL database anonymization, synthetic data generation, and logical dumping.

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1.7k stars60 forks0 contributors

What is Greenmask?

Greenmask is an open-source utility for logical database dumping, anonymization, and synthetic data generation, primarily for PostgreSQL with MySQL support in development. It solves the problem of securing sensitive data in staging, testing, and development environments by providing stateless, compatible backups that can be transformed or subsetted.

Target Audience

Database administrators, DevOps engineers, and developers who need to sanitize production data for non-production use while maintaining referential integrity and compatibility with standard tools.

Value Proposition

Developers choose Greenmask for its deterministic transformations, storage-agnostic backups, and extensibility, offering a robust, drop-in replacement for `pg_dump`/`pg_restore` with advanced data masking and subsetting capabilities.

Overview

Database anonymization and test data management

Use Cases

Best For

Anonymizing PII in staging or testing databases for compliance
Creating referentially intact subsets of production databases for local development
Generating synthetic test data from scratch for empty environments
Replacing `pg_dump`/`pg_restore` with enhanced transformation logic
Managing backups across S3-compatible storage providers
Applying conditional data masking based on specific column criteria

Not Ideal For

Projects relying on MySQL for production-ready data masking (support is still in beta)
Teams needing physical backups or WAL-based point-in-time recovery
Simple backup workflows without data transformation requirements

Pros & Cons

Pros

Referential Subsetting

Creates smaller development databases that maintain cyclic and polymorphic references, ensuring data integrity for testing as highlighted in the database subsetting feature.

Storage Agnostic Backups

Supports local directories and S3-compatible services like AWS S3, MinIO, GCS, and Azure, offering flexible backup management without vendor lock-in.

Deterministic Anonymization

Uses hash functions for reproducible data masking, guaranteeing consistent output for the same input, which is crucial for compliance and testing environments.

Extensible Transformation Engine

Allows custom transformations in any programming language via the CMD transformer or predefined templates, enabling domain-specific data processing.

Cons

Beta MySQL Support

MySQL functionality is labeled as work-in-progress and beta, making it unreliable for production use with MySQL databases, as admitted in the README.

Complex Configuration

Setting up advanced transformations and subsetting requires detailed YAML or JSON configurations, which can be steep and error-prone for quick adoption.

CLI-Only Interface

Lacks a graphical user interface, which may not suit teams preferring GUI-based tools for database management and anonymization tasks.

Frequently Asked Questions

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