TabQueryBench: SQL Query Benchmark for Synthetic Data

The generation of tabular synthetic data has become a strategic tool for companies that need to share information, train artificial intelligence models under privacy constraints, or accelerate analysis prototypes without exposing real data. However, evaluating the quality of this data goes beyond comparing statistical distributions or correlations: what truly matters is whether it preserves the logic needed to answer analytical queries. This is where an approach like TabQueryBench makes sense—a benchmark that uses SQL queries as measures of structural fidelity. Instead of settling for traditional similarity metrics, this benchmark exposes generative models to real questions that a business analyst or business intelligence system might ask: aggregations, conditional filters, logical joins, and distribution tail searches. The proposal reveals significant patterns: although modern models achieve acceptable fidelity in global queries, they falter in local queries, extreme values, or domains with high discrete cardinality. Even the best model barely recovers 40% of actual rare values. This has direct implications for projects that rely on reliable synthetic data, such as those we develop at Q2BSTUDIO. Our team integrates these rigorous evaluations into the design of custom applications and AI solutions for businesses, ensuring that synthetic data not only looks real but also correctly responds to production queries. The combination of AWS and Azure cloud services with controlled generation pipelines allows us to scale these validations, while cybersecurity and the use of AI agents ensure the process complies with regulations. Additionally, our business intelligence services with Power BI benefit from high-fidelity synthetic data to prototype dashboards without risks. Ultimately, TabQueryBench reminds us that the true test of synthetic data is not how it looks, but how it works when questioned.