The article outlines a software library designed for stochastic streaming algorithms, aimed at addressing the challenges of analyzing big data efficiently. Traditional methods for complex queries like count distinct, quantiles, and graph analysis often require excessive computational resources and time. This library leverages streaming algorithms, or sketches, which provide approximate results much faster and with mathematically defined error bounds. This approach is especially useful for real-time analysis, where sketches are often the only viable option. Yahoo has successfully implemented these techniques, reducing data processing times from days or hours to mere minutes or seconds. The library includes a variety of high-quality sketch algorithms, making it suitable for production systems that handle massive datasets. It features adaptors for popular platforms like Apache Hive, PostgreSQL, and Google BigQuery, ensuring compatibility across languages such as Java, C++, Python, Rust, and Go. This integration simplifies system architecture and minimizes the computational resources needed for challenging tasks. The library also offers built-in Theta Sketch set operators, enabling complex set expression calculations rather than just numerical outputs. This capability enhances the accuracy of queries and provides powerful analysis tools, surpassing traditional Include/Exclude methods. Overall, this collection of sketches represents a significant advancement in the field of big data analysis, making it easier and faster to derive insights from large datasets.