{"id":10881,"date":"2022-03-09T17:51:39","date_gmt":"2022-03-09T17:51:39","guid":{"rendered":"https:\/\/www.genrocket.com\/blog\/?p=10881"},"modified":"2026-06-10T14:16:06","modified_gmt":"2026-06-10T14:16:06","slug":"how-to-leverage-metadata-for-synthetic-data-generation","status":"publish","type":"post","link":"https:\/\/www.genrocket.com\/blog\/how-to-leverage-metadata-for-synthetic-data-generation\/","title":{"rendered":"How to Leverage Metadata for Synthetic Data Generation"},"content":{"rendered":"

Enterprise Metadata Management<\/em><\/strong> is technology used to centrally manage and deliver high quality data and trusted information for business analysis and decision-making. Metadata is often referred to as \u201cdata about data\u201d and describes the content, governance, and structure of enterprise information. Metadata is often used to create data catalogs that aggregate, group, and sort multiple data sources to make them accessible for a wide variety of use cases.
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Data Catalogs use metadata to provide a \u201csingle source of truth\u201d, a reference point for maintaining data quality and consistency in a continuously changing data environment.<\/strong> Leading metadata management platforms, such as Abinitio<\/a> and Alation<\/a>, manage the dynamic nature of enterprise data with the use of machine learning to learn and adapt to changes in the data environment.<\/p>\n

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A Perfect Data Model for Synthetic Data Generation<\/h3>\n

Metadata provides a perfect data model for generating synthetic test data. That\u2019s because metadata operates as a continuously updated template of the data structures and data relationships used by data sources across the enterprise.<\/p>\n

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\"Synthetic<\/figure>\n

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This is the same approach used by GenRocket to ingest a SQL database schema, XML Schema Definition (XSD) file, or DDL file to define a synthetic data model. Each one offers a different method for replicating the structure of target data environment as the first stage of GenRocket\u2019s Model\/Design\/Deploy\/Manage<\/em> Test Data Automation Lifecycle<\/a>. GenRocket provides many ways to import or create a data model into its Synthetic Test Data Automation<\/em> platform as illustrated below.<\/p>\n

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\"Data<\/figure>\n

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Modeling Data with JSON Schema Files<\/h3>\n

A\u00a0JSON Schema\u00a0is a standard format that defines what data is required by an application and how to handle that data.\u00a0GenRocket allows software developers and test engineers to import JSON Schema files created by metadata management platforms like Abinitio and Alation to generate synthetic data based on those data definitions.<\/strong><\/p>\n

Because there are so many ways to describe data models using JSON, it is not practical for GenRocket to implement code for each of them. So, using JSON’s Schema Definition Language, GenRocket has defined a JSON Data Definition Language (DDL) as a standard for customers to funnel their JSON formats into GenRocket’s JSON Schema.<\/p>\n

GenRocket’s JSON DDL Schema<\/a> is clean, simple, and contains only the necessary elements to quickly define table definitions to include:<\/p>\n