Tom Ridinger

Join this session to learn about best practices and real-world examples for migrating data engineering and Spark workloads to Snowflake. We’ll cover legacy vs. Snowflake architecture and migration methods such as Iceberg and Delta direct, and we'll share pros and cons of integrating components such as Unity Catalog, Apache Iceberg™ tables and native Snowflake tables. Plus, we'll explore automated code conversion with the Snowpark Migration Accelerator, workflow orchestration with Snowflake Tasks and key data validation and observability techniques to ensure a smooth migration.

Join this session to learn how to successfully move data engineering workloads to Snowflake. We will share best practices and review reference architectures that can ensure success while migrating to Snowflake. Topics include: - Migrating PySpark code - Migrating Notebooks to stored procedures - Data movement: Iceberg tables/Snowflake native tables - Code testing, CI/CD approaches, performance and optimization considerations - Data validation considerations and anti-patterns/considerations