The AI Era Demands More: Accelerate Managed Spark Migrations to Snowflake

At Snowflake, we empower organizations to achieve unparalleled value from their data, going beyond limitations of traditional data warehousing and fragmented, managed Spark approaches. While managed Spark in particular may promise flexibility, organizations often grapple with inherent complexities such as complicated infrastructure and data format management, brittle pipelines, disjointed governance across disparate systems, the burden of building and maintaining a DIY disaster recovery strategy, and challenges in easily leveraging existing SQL expertise for broad analytics.

The Snowflake AI Data Cloud offers a truly unified, easy, connected and trusted platform — whether you're aiming for superior performance and cost-efficiency in data warehousing, seamless lakehouse analytics with integrated security and governance, or accelerating development for all your data and AI workloads. Today, we're thrilled to announce advancements that make unlocking this comprehensive value — and migrating to Snowflake — smoother and faster than ever.

These advancements are particularly impactful for organizations looking to modernize their data strategy for the AI era and consolidate disparate systems. Specifically, for those modernizing data engineering pipelines, we’re announcing new capabilities to accelerate Apache Spark to Snowpark migrations, ensuring you can quickly build and operate your data workloads natively within the Snowflake AI Data Cloud. We’re also introducing powerful enhancements designed to streamline migrations from Databricks SQL to Snowflake, enabling you to harness Snowflake's superior capabilities for both data warehousing and lakehouse analytics. 

Simplify Apache Spark to Snowpark migrations

Complex code translation, effort assessment, managing operational overhead and ensuring data integrity all pose challenges when migrating traditional Apache Spark data engineering workloads. Customers see an average of 5.6x faster performance and 41% cost savings with Snowpark over Spark.¹ To help you navigate these hurdles and unlock the possibilities of Snowpark's unified data processing and advanced analytics, we provide a suite of complimentary tooling and automations designed to simplify your traditional Apache Spark to Snowpark migration. These resources guide you through the entire migration lifecycle, from crucial initial decisions to code conversion to final testing. This comprehensive support includes:

• Clarity in cost planning: Gain valuable insights into your current Databricks environment with the Workspace Estimator. Understand the potential cost reductions from migrating to Snowflake with cost comparisons, all without giving direct system access. Make informed decisions with confidence.

• Faster and more accurate Notebook migration: Transition data science and engineering workflows with the Snowpark Migration Accelerator, which now intelligently translates many Databricks Notebook Magic Commands and Utilities into their Snowpark equivalents.

• Accelerated validation: Ensure the integrity of your migrated data with the Snowpark Checkpoints Library, integrated with the Snowpark Migration Accelerator and the Snowflake VS Code Extension. This powerful feature automatically generates tests to both validate the data in your converted dataframes and help you track where something may have changed, giving you peace of mind.

• Spark and SQL integration: SnowConvert AI's powerful Databricks SQL conversion is now also used by the Snowpark Migration Accelerator to convert SQL in Notebooks and Scripts, alongside Python or Scala code. 

Accelerate Databricks SQL to Snowflake migrations with SnowConvert AI

Migrating from data warehouses is notoriously complex, often involving lengthy timelines and escalating costs. With data ecosystem migration agents powered by Snowflake Cortex, SnowConvert AI is your free, automated solution designed to dramatically reduce the complexities, costs and timelines associated with lakehouse, data warehouse, BI and ETL migrations. Specifically, SnowConvert AI significantly reduces the time needed in various phases of the migration projects: assessments, code conversion, migration and validation. It intelligently analyzes your existing system, automating code conversion and data validation, streamlining the entire migration process.

For instance, converting complex SQL and other objects — commonly a manual, error-prone and time-consuming process requiring specialized expertise — is now made significantly faster and more accurate through the power of data ecosystem migration agents, powered by Snowflake Cortex. 

Building on our existing broad support of data warehouse migrations, we’re excited to announce new SnowConvert AI support for Databricks SQL to Snowflake migrations. This empowers your team or preferred System Integrator (SI) with cutting-edge AI-powered code conversion, offering significant advantages that translate directly into faster modernization and quicker time-to-value:

• Streamlined and comprehensive conversion: Say goodbye to manual, error-prone conversions while migrating from Databricks SQL to Snowflake. You now gain unrestricted access to effortlessly convert your tables, views, and even those crucial DML statements from Databricks SQL to Snowflake — all completely free of charge.

• Accelerated timelines: Migrations notoriously run long and off-schedule, but with SnowConvert AI, you can achieve faster project completion on your terms. Whether you choose to lead the migration internally or collaborate with a trusted SI partner, the power to move faster is now in your hands.

• Unprecedented scalability and automation: No matter the size or complexity of your Databricks SQL environment, SnowConvert AI can handle it. With a track record of over 95% code and object conversion automation in Teradata, Oracle and SQL Server migrations,² we’re excited to now optimize efficiency and accuracy across even the most diverse and extensive data landscapes.

Enhance connectivity for a phased modernization journey

We recognize that for many organizations, a complete migration is a journey. To help you leverage the power of the Snowflake AI Data Cloud while navigating this transition, we are announcing advancements that enhance interoperability between Snowflake and Databricks. 

With the introduction of new Catalog-Linked Databases (GA soon), customers can automatically sync Snowflake Horizon Catalog with Apache Iceberg objects managed by any Iceberg REST Catalog. And with the Snowpark DB-API (PrPr), you can now process data from Databricks with a single command for ad hoc analysis or smaller workloads (up to 100GB). This connectivity bridges the data between platforms, offering flexibility, and allows you to begin integrating with Snowflake’s powerful capabilities into your existing ecosystem, paving the way for a more unified data strategy.

Ready to migrate?

These announcements represent our ongoing commitment to providing you with the most powerful and user-friendly solutions to modernize your data ecosystem. Whether you're focused on migrating your core data warehouse, data lake, or lakehouse, your data engineering pipelines, or simply seeking interoperability during a migration, these enhancements are designed to unlock the future of your data.

Ready to take the next step? Visit the Migrate to the AI Data Cloud page to learn more and begin your accelerated migration today — plus, read stories from customers like Travelpass, who saved 65% in costs by switching from Managed Spark to Snowflake.

¹ Based on customer production use cases and proof-of-concept exercises comparing the speed and cost for Snowpark versus traditional Spark services between November 2022 and May 2025. All findings summarize actual customer outcomes with real data and do not represent fabricated datasets used for benchmarks.

² Based on total lines of code for professional services engagements with SnowConvert AI for entire workloads and not individual objects, specifically for Oracle, SQL Server, and Teradata migrations. Amazon Redshift is excluded. The numbers include internal usage. Data from March 2020 to April 2025.