Synthergy

Synthergy – Synthetic Data Generation Model

Inspiration

The need for high-quality, privacy-preserving data in regulated sectors like energy, healthcare, and finance inspired Synthergy. Traditional datasets pose privacy risks and scalability issues—Synthergy was built to bridge this gap with intelligent synthesis.

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What it does

Synthergy generates synthetic datasets that mirror the statistical structure of original data without compromising sensitive information. It supports CSV/Excel input and produces accurate, type-preserving synthetic data with professional reporting. Designed for usability and extensibility, it enables downstream ML workflows while supporting compliance with privacy standards.

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How I built it

Built using Python, Streamlit, and the YData SDK, the platform integrates fallback statistical synthesis, schema inference, and data-type-aware generation. Key components include modular preprocessing, format detection, privacy-preserving synthesis, and enterprise-grade reporting. It currently supports tabular formats and is optimized for datasets up to 50,000 rows.

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Key Challenges

• Code Quality:  Runtime errors—resolved via audits and usage tracking.
• Data Type Preservation: Issues with incorrect types and categorical loss—addressed through enhanced logic and column-specific handling.
• UI Design: Needed transition from casual to enterprise-grade presentation.
• Feature Overload: Resolved by implementing modular toggles based on user feedback.
• Dependencies: Complex SDK and environment issues mitigated by slimming down requirements and improving isolation.

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Accomplishments • Robust data-type fidelity in synthetic outputs. • Built file reporting in both HTML and PDF. • Deployment streamlining and repository for security. • A clear modular architecture to support extensibility and future development (AWS ECR).

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Takeaway • Regular code audits prevent tech debt. • Intelligent synthesis must handle data types and structure holistically. • User-centric design and feedback loops are crucial for feature prioritization. • Dependency management is vital for reproducibility and scaling.

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What’s next

Focus • Format Support Expansion: Add JSON, Parquet, XML, HDF5, and SQL. • Performance Boost: Optimize processing for large datasets. • UI Enhancements: Dynamic UI for multi-format input handling.

Upcoming Roadmap • Advanced Synthesis: GANs, transformers, and LSTMs for richer outputs. • Scalability: Spark integration, GPU acceleration, cloud-native deployment. • Enterprise Features: REST API, SDKs, SSO, role-based access, and audit logs. • Analytics: Auto-insight generation, synthesis quality predictors, and interactive dashboards.

Synthergy is ready to transition from a local prototype to a production-grade solution for enterprise data science workflows.

Built With

• amazon-web-services
• python
• streamlit
• ydata