Data science teams struggle to derive new insights from massive amounts of data. To accomplish this, teams work with compute environments that hit bottlenecks, and require heavy operational overhead or both—reducing the amount of time spent innovating.
With Snowpark for Python, teams can now code with Python’s familiar syntax and execute with the Snowflake processing engine’s superior performance, security, and near-zero maintenance. This enables data application developers to run complex transformations within Snowflake while taking advantage of the built-in unlimited scalability, performance, governance, and security features.
Join us on 26 October to learn more about:
- Loading data into Snowflake
- Updating existing data and working with semi-structured data in Snowflake
- Writing and executing exploratory data analysis and feature engineering code in Snowflake using Snowpark for Python DataFrames
- Extending the Snowpark API with new functionality
- Using third-party libraries not available in the Snowflake Anaconda channel
- Scalar UDFs, tabular UDFs, and batch API UDFs
Principal Sales Engineer , Snowflake