Snowflake Computing and Saturn Cloud have announced a strategic alliance and integration of products to usher in the highest-speed tooling for data science and machine learning teams. Joint customers of the companies choose the solution because of the ease-of-use of Python and to achieve 100x faster performance over serial Python and Apache Spark. Recently, Saturn Cloud published a random forest benchmark that achieved 2000x faster runtime over traditional tooling. Since launching in 2020, Saturn Cloud has seen over 200,000 hours of compute. In September 2020, Snowflake made a historic IPO on the NYSE, becoming the largest software IPO of all time. Analysts anticipated it to be the largest public offering of the year, with the stock price doubling since IPO. How the integration works: Snowflake publishes native python drivers, the snowflake-connector-python, as an interface to connect Python applications to their data warehouse. Saturn Cloud makes it easy to accelerate these Python applications with Dask and RAPIDS for multi-node multi-GPU computing. Therefore, pairing Snowflake with Saturn Cloud provides the best of both worlds: Snowflake optimizes sql-like operations, and Saturn Cloud scales Python-based workloads like machine learning. As a result, loading Snowflake data into Dask is easy with Saturn Cloud. Once a user has provisioned a JupyterLab instance and Dask cluster in Saturn Cloud's UI, it's just a few lines of code to get started. Read about this here.