Databricks vs Snowflake: Which Data Platform Is Best in 2025?

In a review of 695 user experiences by visual flow, 74% rated both Databricks and Snowflake 4 stars or higher. Yet, these platforms serve different needs in the data world. Databricks grew over 50% last year, while Snowflake saw 31.5% growth – showing the huge demand for both solutions.

Companies now face a tough choice between these two data giants. Each platform brings unique strengths: Snowflake shines in data warehousing, while Databricks excels in data processing and ML workloads. Your choice can impact your team’s productivity and your bottom line.

This guide will help you understand the key differences between Databricks and Snowflake. We’ll compare their features, costs, use cases, and real user experiences. You’ll learn which platform fits best with your team’s skills and business needs.

What is Databricks?

Databricks is a data intelligence platform that combines data processing and machine learning capabilities. The platform runs on Apache Spark and offers a powerful notebook interface for data teams. Key features include:

• ETL (Extract, Transform, Load) processing with strong Python support
• Machine learning workloads with MLflow integration
• Real-time data processing
• Built-in dashboards and visualizations
• Unity Catalog for data governance

Databricks works best for companies that need heavy data processing and machine learning capabilities. The platform started as a managed Spark service and has grown to include SQL warehousing features. Companies use Databricks when they have technical teams who prefer working with Python and Spark.

What is Snowflake?

Snowflake is a cloud data warehouse platform that separates storage and compute resources. The platform focuses on SQL-based analytics and data sharing. Key features include:

• Easy-to-use SQL interface
• Independent scaling of storage and compute
• Built-in data-sharing capabilities
• Marketplace for buying and selling data
• Strong security and governance features
• Native app development support

Snowflake excels at traditional data warehousing tasks and business intelligence. The platform started as an SQL data warehouse and now supports Python workloads through Snowpark. Companies choose Snowflake when they need a straightforward data warehouse that’s easy to manage and scale.

Difference Between Snowflake and Databricks?

Snowflake and Databricks serve distinct but complementary roles in modern data platforms. Snowflake excels in structured SQL-based analytics with its cloud data warehouse, while Databricks leads in handling unstructured data, machine learning, and real-time analytics using its data lakehouse concept. Here’s a detailed breakdown.

Core Purpose and Architecture

Snowflake: Snowflake is a fully SaaS-based cloud data warehouse for structured data analytics. Its architecture separates storage and computing, enabling independent scaling, cost optimization, and excellent SQL query performance. Snowflake emphasizes ease of use and integration with business intelligence (BI) tools.

Databricks: Databricks operates as a PaaS and integrates the Apache Spark framework. Its focus is on data engineering, machine learning (ML), and artificial intelligence (AI). The platform introduced the “Lakehouse” architecture, combining the capabilities of data lakes for unstructured data with the robust querying of data warehouses.

Data Handling and Processing

Snowflake: Optimized for structured and semi-structured data, Snowflake supports JSON, Parquet, and ORC. However, it lacks native capabilities for handling unstructured data. It optimizes data processing for structured and semi-structured data through:

• Excels at SQL queries
• Strong batch processing
• Limited streaming support
• Good for structured data
• Easy ELT workflows

Databricks: Databricks handle structured, semi-structured, and unstructured data. Its Delta Lake adds ACID transactions and real-time processing capabilities. Databricks excels at handling diverse data types with these processing features:

• Advanced ETL capabilities
• Strong streaming support
• Machine learning focus
• Handles all data types well
• Complex data transformations

Performance

Snowflake: Outperforms Databricks in BI and SQL workloads due to its SQL-optimized engine. Features like caching and clustering ensure high-speed queries.It  delivers high-performance analytics with its SQL-optimized engine through:

• Built for BI workloads
• Simple dashboard creation
• Works well with BI tools
• Fast query performance
• Data marketplace access

Databricks: Better for large-scale data transformations and ML workloads. It offers advanced tuning and parallel processing through Spark. Databricks focuses on performance for large-scale operations with:

• Advanced analytics features
• Built-in visualization tools
• ML model deployment
• Real-time analytics
• Comprehensive dashboarding

Machine Learning and AI

Snowflake: Limited built-in ML capabilities but integrates well with external tools like DataRobot or SageMaker. Snowflake supports machine learning workflows with these capabilities:

• Basic ML through Snowpark
• Recent AI feature additions
• Integration with external ML tools
• Container services for ML
• Limited built-in ML features

Databricks: Built for ML, offering MLFlow for model lifecycle management, automated hyperparameter tuning, and real-time model serving. Databricks provides end-to-end machine learning support through:

• Native MLflow integration
• End-to-end ML lifecycle
• Model serving capabilities
• Advanced ML workspaces
• Deep learning support

Cost Structure

Snowflake: Snowflake uses a credit-based system, where credits represent the compute resources consumed. Storage and compute costs are billed separately. Snowflake offers a transparent pricing model with:

• Pay for storage and compute separately
• Per-second billing for compute
• Predictable pricing model
• Built-in cost management tools
• Clear budget controls

Databricks: Databricks employs Databricks Units (DBUs) to measure compute usage. DBU rates vary based on cloud provider, workload type, and cluster configurations. Databricks structure its pricing around cloud resources with:

• Pay for platform and cloud resources
• DBU (Databricks Unit) based pricing
• More complex cost structure
• Requires cloud cost monitoring
• Can optimize costs through tuning

Ease of Use

Snowflake: User-friendly for SQL users with minimal setup time. Best for organizations with existing BI tools and SQL expertise. Snowflake prioritizes user-friendly operations through:

• Simple, SQL-focused interface
• The quick learning curve for SQL users
• Built-in data-sharing features
• Easy third-party tool integration
• Minimal infrastructure management

Databricks: Steeper learning curve but offers robust features for technical users, such as data scientists and engineers. Databricks caters to technical users with advanced features including:

• Advanced notebook interface
• Steeper learning curve
• Strong Python and R support
• Deep customization options
• Requires more technical expertise

Ecosystem and Integration

Snowflake: Connects seamlessly with BI tools like Tableau and Looker. Its marketplace provides access to datasets and third-party apps. It connects with various tools and services through:

• Wide BI tool support
• Strong ETL tool integration
• Native apps platform
• Marketplace integration
• Third-party connectors

Databricks: Integrates with open-source technologies and allows diverse workloads via Python, R, and Scala. Databricks builds a comprehensive ecosystem with:

• Deep cloud integration
• ML tool ecosystem
• Development frameworks
• API connections
• Data source connectors

Data Sharing and Governance

Snowflake: Its marketplace and data-sharing capabilities make collaboration straightforward, though it’s limited to the Snowflake ecosystem. Snowflake ensures secure data management with:

• Strong role-based access
• Built-in data encryption
• Comprehensive audit logs
• Data sharing controls
• Snowflake Horizon features

Databricks: Delta Sharing allows open and secure collaboration across platforms, emphasizing flexibility. Databricks maintains data security and sharing through:

• Unity Catalog
• Column-level security
• Integration with cloud security
• Access control features
• Audit and compliance tools

Choosing between Snowflake and Databricks depends on your specific needs:

• For analytics-focused use cases with structured data, Snowflake is the clear winner, offering simplicity and speed.
• For organizations needing ML, AI, and large-scale unstructured data processing, Databricks excels with its open and flexible architecture.

A hybrid approach may be the best strategy, leveraging Snowflake for warehousing and Databricks for data engineering and advanced analytics.

How Can You Choose Between Snowflake And Databricks?

Snowflake provides a user-friendly data warehousing solution with minimal maintenance, perfect for SQL-focused teams and quick deployment. Databricks offers powerful data science capabilities with Apache Spark, suited for technical teams needing customization. Recent data from Yahoo news shows Snowflake growing at 31.5% and Databricks at over 50% annually in 2024.

Choosing Between Snowflake and Databricks: A Strategic Guide

Team Composition and Technical Requirements

The skillset of your team plays a significant role in selecting the platform. Consider your team’s technical expertise and current workflow. Recent data shows organizations deploying Snowflake solutions in under 10 minutes, compared to longer setup times with Databricks. 

Databricks require more specialized knowledge but provide deeper customization options for technical teams. For organizations with experienced data scientists and engineers, Databricks offers a highly customizable environment.

Pro Tip: Snowflake proves more accessible for teams with SQL backgrounds, offering immediate productivity.

Data Volume and Processing Needs

Your data volume and processing requirements significantly influence platform choice. Snowflake excels in handling structured data warehousing needs up to several petabytes. 

If your workload involves advanced analytics, Databricks’ capabilities in managing varied data formats offer a distinct advantage. According to recent reviews, Databricks shows superior performance for organizations processing massive unstructured datasets, especially when implementing machine learning workflows.

Cost Structure and Resource Management

Snowflake’s cost model is straightforward, with separate charges for storage and compute resources.  Databricks can provide cost advantages for ETL workloads but require more investment in optimization. Factor in the human resources needed for platform maintenance – Snowflake typically demands less overhead.

Pro Tip: Consider both direct and indirect costs. Snowflake offers transparent pricing with serverless tasks costing 10% less than dedicated warehouses.

Consider Workload Complexity

Analyzing the complexity of your workload is essential. Snowflake is optimized for structured, predictable workloads and excels in batch processing and SQL analytics. It performs well in environments with defined data pipelines and reporting needs. 

Databricks, by contrast, thrives in handling large-scale, complex workloads, including streaming data and exploratory data science projects. Its Lakehouse architecture combines the strengths of data lakes and warehouses, providing flexibility for evolving data strategies.

Integration and Ecosystem Compatibility

Integration capabilities often determine platform suitability. Current market analysis shows Snowflake’s marketplace offers over 2,900 datasets and native applications. Databricks emphasizes built-in features like Unity Catalog and integrated dashboarding. Consider your existing tools – Snowflake integrates seamlessly with popular ETL tools like Fivetran, while Databricks offers native Apache Spark compatibility.

Growth Trajectory and Future Needs

Recent data indicates Databricks achieving over $1.5 billion in revenue for FY2024. According to Techcrunch, their SQL product growing 200% year-over-year. Also, it has a market value of around $43 billion. Meanwhile, Snowflake maintains steady growth at 31.5%. 

Pro Tip: Consider your organization’s future direction – Snowflake suits companies prioritizing data warehouse and analytics capabilities, while Databricks aligns with organizations focusing on advanced machine learning implementations.

Hybrid Approach Consideration

Many organizations successfully implement both platforms. Market data shows an increasing overlap in customer bases between Snowflake and Databricks. This hybrid approach allows companies to leverage Snowflake’s robust data warehousing alongside Databricks’ advanced analytics capabilities, though it requires careful architecture planning and resource allocation.

Pro Tip: Start with a clear assessment of your immediate needs while keeping scalability in mind. Consider running a proof of concept with each platform, focusing on your most critical use cases. This approach will provide practical insights into which platform better aligns with your organization’s specific requirements and goals.

FAQs

Who is Databricks’ biggest competitor?
Snowflake is Databricks’ biggest competitor, excelling in structured data analytics compared to Databricks’ focus on diverse workloads.

Which is better Snowflake or Databricks?
Snowflake excels in analytics; Databricks leads in machine learning and real-time processing. Choose based on data needs and goals.

Which ETL tool is good for Snowflake?
Fivetran, Talend, and Informatica are popular ETL tools for Snowflake, offering seamless data integration and transformation.

Which big companies use Databricks?
Major companies like Microsoft, Shell, and Comcast use Databricks for advanced analytics and machine learning solutions.

Is Snowflake a SaaS or PaaS?
Snowflake is a fully SaaS-based platform, offering managed cloud data warehousing services with minimal infrastructure management.

Is Databricks a SaaS or PaaS?
Databricks operates as a PaaS, providing a managed environment for data engineering, analytics, and machine learning tasks.

Final Thoughts

The decision between Snowflake and Databricks requires careful consideration of your organization’s specific needs, technical capabilities, and growth trajectory. Snowflake offers an efficient, user-friendly solution for companies prioritizing SQL-based analytics and straightforward data warehousing, with proven success in rapid deployment and minimal maintenance. 

Databricks presents a compelling option for organizations focused on advanced analytics, machine learning, and complex data processing, backed by impressive growth metrics and extensive customization capabilities. 

According to recent data, both platforms show strong market performance, with Databricks achieving over $1.5 billion in revenue and Snowflake maintaining steady growth at 31.5%. The choice ultimately depends on aligning platform strengths with your organization’s data strategy, team expertise, and long-term objectives.