What are Databricks and Snowflake?
Databricks and Snowflake are two leading cloud-based data platforms that serve different but overlapping use cases. Databricks is optimized for big data processing and machine learning, while Snowflake specializes in data warehousing and analytics. This article compares them in terms of architecture, cost, performance, and ideal use cases.
Architecture Comparison
| Feature | Databricks | Snowflake |
|---|---|---|
| Storage & Compute | Decoupled (Lakehouse model) | Decoupled |
| Data Storage | Delta Lake (open format) | Proprietary optimized storage |
| Compute Model | Spark-based clusters | Virtual warehouses |
| Concurrency | High with autoscaling | Multi-cluster auto-scaling |
Databricks
- Lakehouse Architecture: Combines data lake and warehouse capabilities.
- Optimized for ML & AI: Built-in support for machine learning and deep learning workloads.
- Apache Spark-Based Processing: Uses Spark clusters for distributed data processing.
Snowflake
- Separation of Storage and Compute: Compute scales independently from storage.
- Multi-Cloud Support: Runs on AWS, Azure, and Google Cloud.
- Virtual Warehouses: Optimized clusters for analytics and reporting.
Databricks vs Snowflake Cost
| Pricing Factor | Databricks | Snowflake |
|---|---|---|
| Storage Cost | Based on cloud provider | ~$23 per TB per month |
| Compute Cost | Pay-per-use Spark clusters | Pay per second per virtual warehouse |
| Free Tier | Community Edition available | Time-limited free trial |
- Databricks: Charges are based on Databricks Units (DBUs), which factor in cloud provider, instance type, and workload.
- Snowflake: Uses a pay-per-second model based on virtual warehouse size, making it predictable for BI and analytics workloads.
Performance & Scalability
| Factor | Databricks | Snowflake |
|---|---|---|
| Query Performance | Optimized for Spark workloads | Fast performance with caching and clustering |
| Concurrency Handling | Autoscaling clusters | Multi-cluster compute scaling |
| Indexing & Clustering | Delta Lake optimizations | Manual clustering and partitioning |
- Databricks: Best suited for machine learning, ETL, and large-scale data engineering workloads.
- Snowflake: Optimized for fast analytical queries and high concurrency.
Key Features Comparison
| Feature | Databricks | Snowflake |
|---|---|---|
| Data Sharing | Delta Sharing (open standard) | Secure Data Sharing across clouds |
| Machine Learning | Built-in MLflow, TensorFlow, PyTorch | Requires external ML tools |
| Security & Compliance | IAM, role-based control | Role-based and fine-grained control |
Use Cases
When to Choose Databricks
- Best for big data processing and ETL
- Ideal for machine learning and AI workloads
- Suitable for data lakes and unstructured data
When to Choose Snowflake
- Best for cloud data warehousing and analytics
- Ideal for business intelligence and reporting
- Suitable for high-concurrency workloads
Using Databricks and Snowflake with Evidence
Whether you’re using Databricks or Snowflake, Evidence provides an efficient way to build reports and dashboards from your data warehouse. With Evidence, you can:
- Connect directly to Databricks or Snowflake for seamless data integration.
- Automate reporting workflows and generate insightful analytics.
- Collaborate with your team using a version-controlled reporting framework.
Learn more about using Databricks and Snowflake with Evidence by visiting the Evidence documentation.
