The tool adopts an innovative dialogical interaction design, transforming the traditional data modeling process into a visual three-step operation process. In the business type selection stage, the preset templates of retail/medical/financial have built-in industry-standard field systems, for example, selecting "e-commerce" will automatically include user behavior buried fields. The data schema configuration supports star/snowflake and other complex Schema, and the system will automatically infer the table relationship through natural language understanding.
Key technology realizations are included:
- Dynamic parameter response: When the user selects "Multiple Table Relationships", the interface will intelligently add foreign key configuration options.
- Real-time semantic validation: When you enter "Generate sales data with seasonal fluctuations", GPT-4o automatically supplements the month field.
- Visualization Preview: Sample data generated will highlight key business fields (e.g. masking of sensitive fields such as amounts/dates)
Compared to traditional ETL tools that require the writing of transformation rules, this interaction enables business analysts to create professional-grade test datasets without a technical background, and real-world testing lowers the learning threshold by 70%.
This answer comes from the articleMetabase AI Dataset Generator: Quickly Generate Real Datasets for Demonstration and AnalysisThe
