Migrating from Snowflake to Databricks: The Smart Move Data Leaders Are Making 

Data is something that never has a pause button in an organization. The ever-growing volume of enterprise and external data has increased the need for scalable platforms that can cater to modern, future-looking use cases, and AI-infused business processes grow. Statista’s reports from 2010 have shown that 2025 projection as high as 181 Zettabytes of data has been created globally.  

We have noticed that when enterprises start their data platform modernization journey with a cloud data warehouse, the cost of data processing tends to be higher. Especially, when the volume of data grows from the Gigabyte to the Terabyte range, the cost of data transformation becomes apparent. 

The Reason for Migration? 

Multiple companies are currently using Snowflake, which is a powerful data warehouse, but Databricks on the other side offers greater flexibility for big data, real-time analytics and AI model Building. It outperforms Snowflake when it comes to consolidating data, analytics, and machine learning workflows. Additionally, the recent benchmarks set by Databricks indicate that ETL workloads can be up to nine times more expensive on Snowflake.  

The beneficial aspect of Databricks is that it allows organizations to store and process data using open-source file formats like Delta Lake, which can be more cost-effective than Snowflake’s proprietary data format. Moreover, there is flexibility to scale up and down based on workload requirements, which automatically reduces the compute cost. This platform is built to scale and optimize performance and cost.  

Comparative analysis of Databricks and Snowflake 

Capability	Databricks	Snowflake
Total Cost of Ownership (TCO)	Unified Data Intelligence Platform for ETL, BI, AI/ML, and governance reduce tool sprawl and overall data costs	Consumption-based pricing across separate workloads often increases costs as ETL, BI, and AI scale
Platform Architecture	Single, unified platform for analytics, AI/ML, governance, and operational workloads	Primarily a cloud data warehouse focused on analytics
Openness & Lock-in	Built on open formats, open standards, and open governance (Delta Lake, Unity Catalog) enabling zero lock-in	Relies on proprietary architecture, limiting flexibility across engines and formats
Zero-Copy Data Sharing	Native zero-copy access with governed views via Unity Catalog, eliminating redundant pipelines	Data sharing supported, but many use cases still require duplication across accounts or regions
Unified Governance	Unity Catalog provides a single, open governance layer across data, analytics, and AI models	Governance largely limited to the warehouse layer, often requiring additional tools
AI & Machine Learning	Fully integrated ML lifecycle: data engineering, training, deployment, and low-cost inference	Depends heavily on external tools for advanced AI/ML workflows
BI & Analytics	Integrated AI/BI with Databricks SQL, eliminating the need for separate BI warehouses	BI workloads often require separate virtual warehouses, increasing cost and complexity
Operational + Analytical Data	Lakebase enables real-time operational workloads alongside analytics and ML on the same data	No native support for operational databases; requires separate systems
Data Duplication	Minimal duplication through unified platform and governed sharing	Higher likelihood of duplicated data across tools and pipelines
Scalability for AI Use Cases	Designed for large-scale AI, real-time processing, and multimodal data	Optimized mainly for structured analytical workloads

Why Snowflake Still Falls Behind Databricks 

Often people get confused with the key aspects on why to opt for Databricks over Snowflake. Apart from the features and pricing model, the real differences lie in the platform vision.  

Snowflake was designed initially as a high-performance analytical warehouse, while Databricks was built for data engineering and AI-driven workloads. Furthermore, Snowflake’s ecosystem depends on external tools and integrations to support end-to-end data pipelines, experimentation, and AI deployment. This increases architectural complexity and slows innovation, especially for teams trying to operationalize AI at scale. Databricks, by contrast, enable faster iteration by keeping data, models, and governance within a single, cohesive workflow. 

Though Snowflake continues to enhance analytics, Databricks is pushing deeper into advanced AI, real-time applications, and unified intelligence areas that are becoming essential rather than optional. 

Ultimately, Snowflake works well for reporting and analytics. But organizations aiming to build intelligent applications, operationalize AI, and adapt quickly to future data demands, Databricks offers a more future-ready foundation. 

What Most Migration Guides Don’t Tell You 

Most of the Snowflake to Databricks migration focuses on connectors, copy patterns, and delta conversion. Those migrations often ignore hidden dependencies, organizational resistance, semantic drift, and cost-model shocks that later appear in mid-migration.  

Step 1: Assess Platform & Technical Debt 

• Conduct audit on snowflake assets and discard unused databases, roles, tables and jobs. 

• Identify all downstream dependencies across BI, ETL, Scripts and applications as this helps to detect hidden integrations.  

Step 2: Define Target Lakehouse & Governance 

• Design the Databricks Lakehouse layers including Delta standards, Unity Catalog structure and name the conventions.  

• Make sure to define data ownership, access models, and approve flows to prevent governance issues.  

Step 3: Plan Organization & Skills Transition 

• Start segment users by role and plan targeted enablement across Databricks SQL, notebooks, Jobs, and repos.  

• Establish version control, coding standards and orchestration practices as this helps to enable Databricks as a default program.  

Step 4: Classify & Prioritize Workloads 

• Based on complexity and business value, categorize workloads as lift-and-shift, redesign, or retire based.  

• Execution of the migration can be done with clear rollback plans and stakeholder alignment.  

Step 5: Re-platform SQL & Transformations 

• Migrate simple SQL directly, but redesign complex procedures, tasks, and streams into notebooks, Jobs, or Delta Live Tables. 

• Standardize semi structured data, time zones, and window functions to reduce semantic drift between old and new implementations.​ 

Step 6: Optimize for Databricks Performance 

• Store data in Delta Lake with the right partitioning, file sizing, and selective optimizations instead of reproducing warehouse-style patterns. 

• Implement cluster policies, auto-termination, and a job-first approach to control cost and performance. 

Step 7: Test Rigorously & Run in Parallel 

Define a testing strategy that includes schema checks, row counts, samples, and KPI parity for critical dashboards. 
Run Snowflake and Databricks workloads in parallel and decommission only after metrics stay within agreed thresholds. 

Step 8: Operationalize the Lakehouse 

• Set up observability for pipelines, data quality, platform health, and cost visibility for chargeback.  

• Establish a clear operating structure with releases, reviews, and incident management. 

Step 9: Drive Adoption & Continuous Improvement 

• Enable teams with training, documentation, and reusable patterns to encourage native Databricks adoption. 

• Continuously refine models, consolidate marts, and eliminate legacy patterns of post-migration. 

Sparity’s Migration services 

At Sparity, we view Snowflake-to-Databricks migrations as business modernization initiatives, rather than just lift and shift. 

Our method consists of: 

• Comprehensive analysis of the platforms to identify dependencies that may go unnoticed and assets which are not used. 

• A Lakehouse-first framework designed with best practices from Unity Catalog and Delta. 

• Re-designing workloads to improve performance in accordance with Databricks’ computing styles. 

• Creating a governance model by design, so that self-service security does not impede team progress. 

• Helping teams to successfully adopt Databricks natively rather than as an alternative tool. 

Conclusion 

Databricks is more than just a ‘next-generation’ data worksheet option from SQL Server – it’s about fundamentally changing your view of the value data creates for your organization in the present and future. The ability to orchestrate all the different functions behind managing and gaining insight from your data into one, integrated, high-performance solution is the essence of Databricks. 

While Snowflake has its purpose as a traditional reporting platform, it can’t provide a sophisticated AI solution with real-time processing or a single-platform model for managing all aspects of your data narrative. By migrating with focus on economic and governance efficiency and user onboarding, organizations can benefit from increased innovation speed, lower TCO and increased agility in their future efforts – all of which provide substantial competitive advantages. 

Sparity helps organizations migrate from Snowflake to Databricks with clarity, control, and confidence.