What are BigQuery and Snowflake?
BigQuery and Snowflake are two of the most popular cloud data warehouses, each offering unique strengths for different use cases. This article compares them based on architecture, pricing, performance, scalability, and key features to help data teams choose the best option.
Architecture Comparison
| Feature | BigQuery | Snowflake |
|---|---|---|
| Storage & Compute | Shared | Decoupled |
| Data Storage | Google Cloud Storage (columnar format) | Proprietary optimized storage |
| Compute Model | Serverless | Virtual warehouses |
| Concurrency | Managed by Google | Multi-cluster auto-scaling |
BigQuery
- Serverless Model: Compute is fully managed by Google, eliminating infrastructure concerns.
- On-Demand and Flat-Rate Pricing: Pay per query or reserve compute slots.
- Standard SQL Support: Includes extensions specific to Google Cloud.
Snowflake
- Separation of Storage and Compute: Users can scale storage and compute independently.
- Multi-Cloud Support: Available on AWS, Azure, and Google Cloud.
- Virtual Warehouses: Users can define and optimize compute clusters.
Pricing Comparison
| Pricing Factor | BigQuery | Snowflake |
|---|---|---|
| Storage Cost | ~$0.02 per GB per month | ~$23 per TB per month |
| Compute Cost | $5 per TB scanned | Pay per second per virtual warehouse |
| Free Tier | 1 TB free query per month | Time-limited free trial |
- BigQuery: Charges per query, making it cost-efficient for ad-hoc analytics but potentially expensive for frequent queries.
- Snowflake: Uses a pay-per-second model based on warehouse size, better for workloads with consistent demand.
Performance & Scalability
| Factor | BigQuery | Snowflake |
|---|---|---|
| Query Performance | Optimized for batch queries | Fast performance with caching and clustering |
| Concurrency Handling | Automatic scaling | Multi-cluster compute scaling |
| Indexing & Clustering | Automatic optimizations | Manual clustering and partitioning |
- BigQuery: Best suited for large-scale analytical workloads with occasional queries.
- Snowflake: Optimized for real-time analytics and complex transformations.
Key Features Comparison
| Feature | BigQuery | Snowflake |
|---|---|---|
| Data Sharing | Native data sharing within Google Cloud | Secure Data Sharing across multiple clouds |
| Machine Learning | Built-in with BigQuery ML | Requires integration with external tools |
| Security & Compliance | IAM-based access control | Role-based and fine-grained control |
Use Cases
When to Choose BigQuery
- Best for teams using Google Cloud
- Ideal for serverless architecture with minimal management
- Great for ad-hoc analytics and batch processing
When to Choose Snowflake
- Best for multi-cloud deployments
- Ideal for high concurrency and real-time workloads
- Suitable for data sharing across organizations
Using BigQuery and Snowflake with Evidence
Whether you’re using BigQuery or Snowflake, Evidence provides an efficient way to build reports and dashboards from your data warehouse. With Evidence, you can:
- Connect directly to BigQuery 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 BigQuery and Snowflake with Evidence by visiting the Evidence documentation.
