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AI-grade star schema support for synthetic data generation

Generate synthetic tables based on a single shared table for retaining correlations across your star schema.

Star schema architecture

In the context of modern organizations, data ownership often spans various departments, and data resides in multiple data sources, organized in a star schema. These sources may employ the same technology, such as a common database vendor, or they may utilize different technologies, including various databases and data lakes.

Traditionally, this data is organized using a star schema data model, a prevalent approach in the data field. In a star schema, data are structured with a central fact table containing transactional or measured data, surrounded by smaller dimensional tables that hold attributes about the data. This arrangement resembles a star, with the fact table at the center and the dimensional tables radiating outwards, hence the name “star schema”.

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Star schema in practice

The star schema approach is deployed in various data-intensive industries, such as insurance, to facilitate deep analytics and reporting. When employed in the insurance sector, the star schema organizes data to centralize policy and claims information within a fact table, while the dimension tables capture valuable attributes pertaining to customers, policies, and claims. This structural design empowers insurers to efficiently analyze and extract insights from their data, enabling more precise and informed decision-making.

Star schema support for synthetic data generation

Our AI-grade star schema support applies star schema principles to synthetic data generation. It goes beyond the typical two-table setup by allowing the generation of tables based on a single shared table. The primary objectives are to maintain relationships and correlations between tables within a star schema independently and to enhance the accuracy of your synthetic data.

In practical terms, AI-grade star schema support means that each table is trained in the context of its preceding tables. When new tables are generated, they are created with the knowledge of their predecessors. For instance, consider the following star schema:

Each table in this schema is generated sequentially, building upon the context of the tables that came before it. This process ensures the preservation of relationships and context, ultimately improving the accuracy and effectiveness of your synthetic data.

With AI-grade star schema support, your organization can seamlessly handle complex data structures.
Learn more about AI-grade star schema support in our Documentation.


Retain relationships and correlations between tables in a star schema independently of each other.
Maintain the integrity and increase the accuracy of your synthetic data.
Reduce the risk of data inconsistencies within your synthetic dataset.

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