Brandon Carver

In this hands-on lab, you will learn how to develop, deploy, orchestrate and monitor PySpark applications on Snowflake. Discover how to execute heavy-lifting Apache Spark jobs directly where your data lives, eliminating siloed platforms and risky data movement. You will migrate existing code, automate pipelines and apply unified governance. Join us to see how our fully managed runtime simplifies your architecture, allowing you to focus on insights rather than infrastructure management.

Want to migrate to Snowflake in less than 90 minutes? Come to this hands-on lab. We will work through Snowflake's migration strategy and automation tools — such as SnowConvert and Snowpark Migration Accelerator — to assess, move and validate an entire pipeline with data into Snowflake. Our data will be in another data warehouse, in a data lake and even on premises. Can we move it into Snowflake? Our pipelines are written in Jupyter notebooks utilizing Spark. Can those fully exist in Snowflake? Can all of it happen in Snowflake? Let's find out.