Test Data · 2024-03-26 · 2 min read
Synthetic Data Generation – Full Report
Following our two-part deep dive into Day-Zero synthetic data generation for capacity and performance testing—covering Part 1: Plaitpy and Part 2: Synner—we compiled our findings, distribution benchmarks, and YAML/JSON templates.
Key Takeaways from the Report
- Day-Zero vs. ML-Projected Data: Most synthetic data frameworks (like SDV) focus on learning distributions from existing production tables. When testing a greenfield system or new trading desk, you need declarative Day-Zero synthesis based on business volume projections.
- Plaitpy (YAML-driven): Ideal for engineering teams who want lightweight, version-controlled YAML templates that slot directly into CI/CD pipelines.
- Synner (Visual + JSON): Ideal when data distributions have complex conditional dependencies across multiple columns and you want immediate visual feedback while designing the statistical model.
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