Using Snowflake Notebooks To Build Data Engineering Pipelines With Snowpark Python And SQL

Caleb Baechtold, Principal Architect at Snowflake, demonstrates how to use Snowflake Notebooks (currently in Private Preview) to build data engineering pipelines using a mix of Snowpark Python and SQL. Snowflake Notebooks provide a native development experience directly in the Snowsight UI. Notebooks allow analysts, data scientists, and data engineers to streamline their workflows, develop AI/ML models out of the box with direct access to flexible, distributed compute, and accelerate moving from prototype to production across teams, all directly in the Snowflake platform. Machine learning practitioners in particular can benefit from how easy it is to get started processing data, building models, and deploying to production all natively in Snowflake using Snowflake Notebooks. For a detailed walk through of all of the steps shown in this video, check out the Quickstart Guide here: https://quickstarts.snowflake.com/guide/data_engineering_pipelines_with_snowpark_python/index.html?index=..%2F..index#0 Watch Snowflake's Jeremiah Hansen offer a step-by-step tutorial: https://www.youtube.com/watch?v=yTUneS1WXao Subscribe for more! http://www.snowflake.com/YTsubscribe/ Explore sample code, download tools, and connect with peers: https://developers.snowflake.com/