Successfully Migrating a Snowflake Data Warehouse to the Databricks Data Intelligence Platform


TRACKData Strategy and Lakehouse Implementation
TECHNOLOGIESDatabricks Experience (DBX), Apache Spark, ETL, Governance
SKILL LEVELIntermediate

With the massive influx in Generative AI applications, organizations are going through a critical phase of data infrastructure modernization, laying the foundation for the future, and adapting to support growing data and AI needs. Many organizations that embraced cloud data warehouses (CDW), such as Snowflake, have ended up trying to use a data warehousing tool for ETL pipelines and data science purposes. These organizations likely have observed poor performance and limited capabilities due to the SQL-first implementation of these CDWs and limit their future analytical solutions.


Realizing the limitation and pain with cloud data warehouses, organizations are turning to a Data Intelligence Platform architecture. Though a cloud platform to cloud platform migration should be relatively easy, the breadth of the Databricks platform provides flexibility, and hence requires careful planning and execution. In this session, we present guidance on the migration methodology, technical approaches, automation tools, product/feature mapping, a technical demo and best practices using real-world case studies for migrating data, ELT pipelines and warehouses from Snowflake to Databricks.


Kevin Barlow

/Sr. Solutions Architect