This article discusses a library of stochastic streaming algorithms designed for fast approximate analysis of big data. It highlights the library's ability to handle complex queries efficiently, reducing processing times significantly while maintaining mathematically proven error bounds. Adaptors for various platforms and languages are included to facilitate integration.