Snowflake to Databricks Migration Strategy: When & Why Enterprises Should Switch

The nature of enterprise data platforms is changing fast, and leaders of business are posing the right questions as to whether their existing analytics ecosystem can keep supporting scale, AI adoption, governance, and cost efficiency. With increasing workloads and the growing importance of AI as a key differentiator, the need for faster, more complex-free data platforms is gaining momentum.

The global data lakehouse market is expected to grow from $14.2 billion in 2025 to $105.9 billion by 2034, at a compound annual growth rate (CAGR) of 25%, underscoring the rapid enterprise shift toward unified architectures like Databricks’ Lakehouse.

The change is driving numerous organizations to consider a Snowflake to Databricks migration strategy, particularly because the Databricks Unified Lakehouse has since taken off as an ecosystem that has the potential to consolidate data warehousing, data lakes, machine learning, and governance in a single system.

To companies interested in going beyond conventional analytics and into AI-based automation and real-time intelligence, switching Snowflake to Databricks can bring an increase in efficiency, a decrease in operational complexity, and a substantial decrease in the overall cost of ownership.

Why Enterprises Are Rethinking Analytics Platforms

The data landscape is much different today than it was five years ago. Organizations now require:

• Advanced machine learning capabilities

• Multi-format data processing

• GenAI-friendly architecture

• Real-time insights

• Automated governance

• Scalable cloud architecture

• Lower compute and storage cost

Snowflake remains a robust data warehousing cloud solution, yet several companies are becoming too large for its framework as their application transitions move into advanced analytics and intelligent automation.

In comparison, Databricks provides a unified, end-to-end experience in data intelligence – eliminating the use of various integrated tools and environments.

For deeper insights on managing platform expenses, explore our guide on Snowflake cost optimization strategies for smarter enterprises.

Snowflake vs Databricks: Key Differences Enterprises Should Know

Understanding the difference between Databricks and Snowflake is essential before planning a migration.

Snowflake

• Pure cloud data warehouse

• SQL-first

• Optimized for BI reporting

• Higher storage + compute cost

• Limited advanced ML and AI capability

Databricks

• Databricks’ Lakehouse architecture

• Combines data lake + warehouse

• AI, ML, and LLM native workflows

• Real-time analytics

• Multi-language support (SQL, Python, Scala, R)

Why Move from Snowflake to Databricks?

Business-driven and technology-driven factors that make enterprises decide to migrate to Snowflake over Databricks are numerous:

Lower TCO compared to Snowflake

Databricks reduce per-query cost, storage overhead, and operational spending by eliminating multiple analytics tools.

Unified analytics platform

No need for separate AI/ML engines — Databricks integrates everything.

High-volume unstructured data support

Ideal for streaming, IoT, ML, and real-time data.

AI + ML at scale

Enterprises can deploy sophisticated models without exporting data outside the platform.

Future-proof data governance

Unity Catalog simplifies security, lineage, and compliance.

Faster performance for complex analytics

Delta Lake enables AI/ML workloads and streaming at high scale.

For enterprise data teams, this creates faster time-to-value and reduced operational friction.

When Is the Right Time to Migrate?

A Snowflake to Databricks migration strategy typically becomes a priority when:

Your data workloads are expanding

Expanding pipelines need a platform capable of scaling all the way to warehouse + lake + AI. With the ability to easily combine both structured and unstructured data without costly transformations, Databricks enables enterprises to start processing petabyte-scale workloads and other types of data.

You require machine learning + advanced analytics

Snowflake might need third-party tools, which will add complexity and costs. Databricks introduces ML/AI to the same platform, eliminating data transfer and latency.

You want multi-cloud flexibility

Databricks is available on AWS, Azure, and Google Cloud, and offers vendors long-term agility and less vendor lock-in.

You want to reduce siloed architecture

Databricks avoid the process of data transfers between platforms. Lakehouse architecture allows organizations to integrate BI, ML, and governance layers into one system.

Your analytics spend is increasing

Organizations often see 40–60% cost savings with Lakehouse migration journeys due to optimized storage, compute models, and reduced tool fragmentation.

Migration timing often aligns with transformation planning, cloud strategies, and analytics modernization initiatives.

Core Pillars of a Successful Snowflake to Databricks Migration Strategy

A well-executed migration roadmap focuses on 5 pillars:

Architecture readiness

Assess current Snowflake pipelines, storage, workloads, and dependencies to determine migration complexity and potential performance improvements.

Data discovery + mapping

Determine structured, semi-structured, and unstructured data sources. Databricks provides file format and ingestion flexibility as a basic element, which is much more scalable to data mapping.

Governance + security planning

Move to Unity Catalog using lineage mapping and access rules. Security requirements should be aligned at an early stage by the enterprises to prevent gaps in compliance.

Performance optimization

Optimize SQL + ML workload data processing. Delta Lake enables businesses to unlock higher query performance, more intelligent caching, and enhanced indexing.

>Testing + validation

Prove performance, reliability, and accuracy at all workloads. Validation is used to make sure that the output of the post-migration environment is conforming to the Snowflake benchmarks and business SLA.

Best Migration Approach: Step-by-Step Lakehouse Journey

The best migration approach focuses on a phased lakehouse transition:

Phase 1: Strategy

Establish modernization goals and determine platform preparedness. This involves the assessment of existing Snowflake consumption patterns, BI needs, governance policies, storage formats, and analytics workloads. Decision-making is driven by clear goals, e.g. cost reduction, ML enablement, faster reporting, or real-time streaming.

Phase 2: Architecture

Create the Databricks Lakehouse and set up the Delta Lake + Unity Catalog. This stage forms the basis of security, lineage, metadata control, role-based access and multi-cloud support. Long-term scalability and interoperability is also provided by architecture planning.

Phase 3: Data migration

Convert Snowflake tables, schemas, metadata and pipelines to the Databricks ecosystem. This can involve reformatting file types, lineage mapping, schema alignment validation and ingestion logic restructuring.

Phase 4: Optimization

Tune ingestion, orchestration, and query performance. Delta Lake offers robust indexing, caching, and partitioning, improving efficiency across analytics and ML workloads.

Phase 5: ML + BI enablement

Implement high-order analytics models and develop dashboards. This action converts migrated data into business intelligence, forecasting, predictive insights, and machine learning results.

Phase 6: Expansion

Automatize reporting, governance, lineage, and AI coordination. Enterprises are able to start expanding to other teams, use cases, and cloud regions.

This step-by-step plan ensures a low-risk migration while maximizing enterprise value.

Conclusion: Your Data, Your Future

Migration of Snowflake to Databricks is not just a platform choice but a business plan to consolidate analytics, speed up the adoption of AI, and save money and increase the value of data in the long run.

BluEnt facilitates this change through end-to-end migration planning, architecture alignment, Delta Lake set-up, data pipeline redesigning, ML enablement and governance integration. With its professional enterprise cloud data services, BluEnt assists companies in making a seamless transition to the Databricks Unified Lakehouse and achieve better performance, simplified analytics workloads, and ROI.

Whatever your preference is, be it a gradual migration process or a comprehensive modernization program, BluEnt offers the engineering skills, platform familiarity and the strategy which is required to achieve a successful result.

Start exploring Databricks migration support.

FAQs

What is the main difference between Snowflake and Databricks?Snowflake is a cloud data warehouse, while Databricks is a unified lakehouse platform integrating AI, ML, SQL analytics, and unstructured data processing.

Why should enterprises migrate from Snowflake to Databricks?To reduce operational cost, increase performance, unlock AI + ML, and unify analytics into a single architecture.

How long does a Snowflake to Databricks migration take?Timelines vary from a few weeks to several months depending on data volume, transformation scope, and analytics complexity.

Is Databricks better for machine learning and AI?Yes — Databricks is specifically engineered for ML/AI workloads, including LLM training and predictive modeling.

What is the best migration approach?A phased migration that includes architecture design, data conversion, optimization, governance, and ML integration.