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Snowflake vs Databricks are top cloud data platforms, each with unique strengths. Snowflake leads in structured data and BI workloads, while Databricks excels in big data, AI, and machine learning. This guide compares their architecture, use cases, and costs.
Data is at the heart of modern business decisions. From data warehouses powering reports to machine learning models predicting trends, organizations need platforms that can handle vast, varied cloud data efficiently.
Snowflake and Databricks lead this space, but with distinct approaches — Snowflake focuses on data warehousing and analytics, while Databricks excels in data engineering and advanced analytics.
This guide compares Snowflake vs Databricks so you can identify which platform fits your data needs in 2025.
Snowflake and Databricks are often compared because both operate in the cloud data space, but their core architectures and use cases differ. They can complement each other or compete, depending on the workload and business requirements.
Snowflake is a cloud data warehousing platform that provides a highly scalable, multi-cluster environment for structured data and some semi-structured formats like JSON or Avro. It separates compute and storage, using virtual warehouses to process queries while keeping data in a central storage layer.
“SNOWFLAKE calls its clusters/computes as a virtual Warehouse - It's called 'Virtual' because it's not a real warehouse, It is dynamically created, resized, terminated and resumed on demand. SNOWFLAKE allows the developer to choose the compute by T-shirt size (XS, S, M....) rest snowflake handles!” — LinkedIn Post
Snowflake is designed for business intelligence, reporting, and advanced analytics use cases. Its strengths include:
Secure data sharing capabilities for internal teams and partners.
High concurrency for SQL queries and BI workloads.
Granular access control to manage access data at various levels.
It integrates well with visualization tools, making it a strong choice for business analysts who primarily work with structured datasets.
The Databricks Lakehouse Platform combines the flexibility of a data lake with the reliability of a data warehouse. Built around Apache Spark, Databricks supports a wide variety of data types and is highly suited for data engineering, data science, and machine learning workflows.
“Databricks is the IKEA of Data Engineering”- You walk in with raw materials (CSV files, databases, JSONs everywhere) and You leave with a beautifully assembled 💡 data product: dashboards, ML models, alerts — ready to impress your CEO. — LinkedIn Post
Databricks provides:
Delta Lake for ACID transactions on large-scale data.
Delta Engine for optimized performance.
Delta Live Tables for automated pipeline management.
Feature Store and Model Registry for managing machine learning models.
Built-in capabilities to track experiments during ML development.
It supports multiple programming languages, including Python (supports Python), R, Scala, and SQL, making it attractive for data scientists who need flexibility in processing large datasets and unstructured data.
While both platforms deal with cloud data, their storage and processing philosophies differ significantly.
Snowflake uses a fully managed architecture where compute is provisioned through virtual warehouses and data is stored in a centralized, optimized storage layer. This model is efficient for running large-scale SQL queries without worrying about infrastructure management.
Databricks, on the other hand, follows a lakehouse architecture, leveraging Delta Lake as the storage layer. It can process big data workloads using Apache Spark, making it suitable for large-scale data processing and analytics.
Snowflake focuses on optimized query performance for structured data, allowing business analysts to generate business intelligence insights quickly.
Databricks is designed for heavy data engineering tasks, real-time data pipelines, and graph processing for specialized analytics.
Snowflake works best with structured data, but also supports semi-structured formats. It’s ideal when data originates from transactional systems and needs to be queried for reports.
Databricks excels with unstructured data (video, text, logs) and large datasets that require advanced machine learning features and advanced analytics.
Machine learning and predictive analytics are becoming standard requirements for many businesses.
Snowflake isn’t inherently a machine learning platform but integrates with other tools like AWS SageMaker, DataRobot, and Azure ML. SQL users can run machine learning features through these third-party integrations, making it possible to process data and build predictive models without leaving the BI environment.
The Databricks Platform has native ML capabilities, including MLlib in Apache Spark, making it suitable for training machine learning models directly within the environment. This setup supports machine learning workflows that require deeper analysis of big data.
It also allows data scientists to use GPU-based compute resources for training, store features in the Feature Store, and deploy models at scale.
Sharing data securely across departments and organizations is a top priority.
Snowflake enables secure data sharing via its secure data sharing functionality, letting partners and clients access datasets without moving them. Its granular access control ensures compliance and data governance.
Databricks uses Delta Sharing, an open source project that allows cross-platform, real-time sharing of cloud data. It supports avoiding vendor lock-in, which is beneficial for organizations working with multiple vendors.
Pricing often influences the Snowflake vs Databricks decision.
Snowflake charges separately for compute and storage, making it cost-effective for predictable workloads like BI workloads. Its virtual warehouses can be paused to save costs, and it works well for periodic ETL tool operations.
Databricks also separates compute and storage pricing, but it’s designed for constant data processing and data pipelines at scale. It’s better suited for high-volume big data workloads.
When comparing Databricks vs Snowflake, the choice often comes down to workload type.
Snowflake: Stronger for data warehouse workloads, structured data, and business intelligence.
Databricks: Stronger for data engineering, data science, and machine learning models on data lakehouse architectures.
Here’s how they stack up:
| Feature / Parameter (compare parameters) | Snowflake | Databricks |
|---|---|---|
| Primary Focus | Data warehouse | Data lakehouse |
| Best for | BI workloads, ETL tool, structured analytics | Machine learning, data engineering, big data workloads |
| Storage | Centralized with virtual warehouses | Delta Lake and storage layer flexibility |
| Vendor Lock In | Higher risk | Lower due to open source project and delta sharing |
| Programming Languages | Primarily SQL | Multiple (supports Python, R, Scala, SQL) |
Both platforms have strong integration capabilities.
Deep connections with cloud data warehousing ecosystems.
Native integrations for BI workloads and ETL tool pipelines.
Works with a broad open source platform ecosystem.
Supports delta sharing with external systems, other tools, and third-party integrations.
The competition between Databricks and Snowflake continues to evolve.
Snowflake is adding more machine learning features and unstructured data support.
Databricks is strengthening business intelligence and SQL capabilities with Databricks SQL.
Snowflake support for more AI-driven workloads is expanding.
Databricks' lakehouse platform is positioning itself for hybrid workloads to attract more business analysts.
Both Snowflake and Databricks are leaders in the cloud data space, but they serve different purposes. Snowflake is optimized for data warehouse tasks, making it ideal for business intelligence and reporting. Databricks excels at data engineering, machine learning, and large-scale data processing.
The right choice depends on whether your focus is on analytics for structured data or advanced data science and engineering on unstructured data. Many organizations find value in using both Snowflake for BI and Databricks for complex analytics — creating a combined strategy that meets diverse data needs.
