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Databricks vs. Snowflake

ETL costs up to 9x more on Snowflake than Databricks Lakehouse

Download the migration guide

ETL workloads are the foundation of your analytics and AI initiatives and typically account for 50% or more of an organization’s overall data costs. The rapid rise of LLMs and other AI applications is forcing companies to take a closer look at how to scale in a cost-efficient manner.

Databricks vs. Snowflake

The best data platform is a lakehouse

Use case



Support for all data types

No support for unstructured data

Unified experience across all workloads

No support for DSML

Unified governance

Structured data only

Strong price/performance at any scale

Unfavorable nonlinear price/performance

Real-time data

Requires connectors

Open data sharing

Walled garden approach

Find out why companies are choosing
Databricks Lakehouse over Snowflake

Snowflake to Databricks Migration Guide graphic image

Snowflake to Databricks Migration Guide

Building ETL pipelines or implementing machine learning on Snowflake requires you to manage and operate additional tools. Over time, your architecture will become more costly and complex. With the Databricks Lakehouse Platform, you get high-performing, cost effective ETL and native support for AI. 

Download this migration guide to learn:

  • 5 critical phases of your migration project

  • Best practices to scale your lakehouse

  • Resources to help with your migration journey