What is the Synthesized Platform?

A comprehensive test data transformation platform that provides secure, privacy-preserving versions of production like data for development, testing, and data engineering.

The platform works with relational databases that contain complex structures such as constraints and tables with references to other tables. Relational databases are the central point of many applications, and getting access to data can be crucial for developing, maintaining and testing data-centric applications.

Read more about the challenges: Will Your Data Pass the Test?

Platform Components

Table 1. Architecture Overview
Component Description Key Feature


Data Transformation Engine

Java-based application performing data masking, generation, and subsetting

Best-in-class performance


Web Interface

User-friendly interface for workflow management

Streamlined collaboration


APIs

RESTful APIs for automation

Pipeline integration


CLI

Command-line interface

CI/CD ready

The platform introduces a layer of collaboration and governance to YAML-configured data transformation workflows, enhancing the process of producing production-like data in adherence to transformation rules and policies.

Supported Databases

Governor easily connects to a variety of data sources.

POSTGRES ORACLE MYSQL MARIADB SQLITE MSSQL

Key Capabilities

Table 2. Platform Strengths
Capability Description


Unified Workflow Management

Simplifies and centralizes workflow management and monitoring across all data transformation operations


Embedded Compliance

Encapsulates compliance requirements within data transformations through YAML configuration, facilitating auditing and ensuring reusability


Enhanced Collaboration

Supports collaboration and workflow approval processes for data compliance


Best-in-Class Performance

High-performance data generation and transformation capabilities for large-scale datasets

Who the Platform Helps

Target Users & Decision Makers
Role Primary Benefits Key Activities


Software Engineers & Developers

Get realistic test data without waiting for sanitized production dumps

Development, unit testing, integration testing


QA Engineers & Testers

Test at scale with production-like data that’s safe to use

Functional testing, regression testing, UAT


DBAs

Quickly provision databases for development and testing environments

Database provisioning, environment management


Data Engineers

Test data pipelines with realistic, compliant datasets

Pipeline validation, ETL testing


Compliance Officers

Ensure generated data adheres to compliance requirements

Compliance auditing, policy enforcement


DevOps Teams

Automate test data creation in CI/CD workflows

Automation, continuous integration
The platform is typically owned by DBA Leads, Heads of Data Architecture, VP of Engineering, VP of Quality Engineering, and Tech Lead Managers.

How the Platform Works

The platform allows users to define and run workflows through:

  • Web Interface - User-friendly YAML editor for creating and managing workflows

  • API - RESTful API for programmatic workflow creation and automation

  • CLI - Command-line interface for script-based and CI/CD integration

Workflows connect to a database, extract the database model, learn all necessary information from the database, and generate a transformed copy that will be written to a destination database. These workflows are accessible in a human-readable YAML format and can be previewed within a sandbox database, enabling thorough review of compliance requirements before approval.

While the new database preserves the high-level information of the source database, it is free of sensitive information and preserves data privacy as no original data is present in the generated dataset.

The following information is preserved from source database to target database:

  • Tables and column names - The schema of all tables and columns will be copied from the original source

  • Data types - All columns in the destination database will have the same data type as in the source database

  • DDL - The DDL for both databases (including constraints, procedures, views, sequences, etc.) will be present in the destination database as they were in the source database

  • Referential Integrity - Primary and Foreign keys will be copied, ensuring referential integrity is preserved so that users can query data with join statements and obtain similar results

  • Key Cardinality - Foreign key distributions are generated as close as possible to the source database to ensure similar cardinality

  • Column marginal distributions - Column marginal distributions are approximated and sampled from probability distributions to be similar to those in the original database