Hands on Lab: Snowpark Pandas

What To Expect

Pandas is a go-to tool for data professionals, but when your data explodes in size, your local machine can’t keep up. That’s where pandas on Snowflake changes the game. It’s not just a feature; it’s a revolution for your workflows.

Imagine taking the pandas code you know and love and running it at the speed and scale of Snowflake. By integrating the powerful Modin library, your operations are automatically distributed across Snowflake’s elastic compute engine. No more struggling with memory errors or slow processing. Your code gets translated into optimized SQL queries, executed directly on your data in the cloud.

With pandas on Snowflake, you don’t just get speed; you get a complete solution:

• Unmatched Scalability: Effortlessly handle terabytes of data without ever pulling it into local memory.
• Blazing-Fast Performance: Accelerate your pandas operations by tapping into Snowflake’s optimized, distributed architecture.
• Effortless Integration: Keep your existing code and expertise. Just a few lines of code connect you to the full power of Snowflake.
• Ironclad Security: Your data remains securely within the Snowflake environment, fully protected by its robust governance features.

What You’ll Build

In this engaging session, you’ll discover how to:

• Connect and Read Data Efficiently: Establish a Snowflake session and use pd.read_snowflake to efficiently load a dataset avoiding in-memory limitations.
• Inspect and Transform Data: Examine dataframe properties like shape and descriptive statistics, and perform common data transformations, which are automatically translated to SQL for optimized performance on Snowflake.
• Save Results to Snowflake: Discover how to save your processed pandas DataFrames back into Snowflake as new tables or views.
• Work with Files: Read and write common file formats like CSV, Parquet, and Excel directly from Snowflake stages or local file locations

Who should attend?
Data scientists, data engineers, and analysts who use pandas and want to process larger datasets, improve performance, and build robust data pipelines directly within the Snowflake Data Cloud.