Discover how StarQube’s proprietary NoSQL database outperforms PostgreSQL by up to 63x in query speed while using 90% less storage space

Choosing the right database architecture is critical for financial use cases requiring rapid data access and massive historical datasets. This comprehensive benchmark study compares StarQube’s proprietary NoSQL database with PostgreSQL—one of the most widely used SQL databases—across 582 million rows of financial data. The results demonstrate dramatic performance advantages. These improvements directly translate to faster backtests, portfolio optimizations, and investment analysis workflows.

Beyond raw speed, this white paper explains the fundamental architectural differences between SQL and NoSQL databases and why NoSQL excels for financial time series data. StarQube’s database features a unique three-dimensional key structure (instrument ID, value date, as-of date) that enables point-in-time historical accuracy—essential for avoiding classic overfitting pitfalls. Whether you’re managing terabytes of market data, running complex portfolio simulations, or building quantitative research platforms, understanding these database performance trade-offs is essential for infrastructure decisions that impact daily workflows and computational costs.

What You’ll Learn

• Head-to-head performance benchmarks showing StarQube’s 63x advantage in cross-sectional queries and 10x storage compression on 582M rows
• Why NoSQL beats SQL for financial data: architectural differences that matter for time-series analysis, backtesting, and portfolio optimization
• Real-world performance implications including detailed query speed comparisons, variability analysis, and what this means for daily workflows
• Technical deep-dive into StarQube’s three-dimensional data model, point-in-time versioning, and how it prevents look-ahead bias
• Complete benchmark methodology with reproducible test parameters, hardware specs, and query types tested