This post is maintained by Bytebase, an open-source database DevSecOps tool that can manage both Snowflake and Redshift. We update the post every year.
| Update History | Comment |
|---|---|
| 2025/04/18 | Initial version. |
Why Comparing Snowflake and Amazon Redshift
When comparing Snowflake and Amazon Redshift, we're examining two cloud-native data warehouse solutions designed for large-scale analytics and business intelligence workloads. Both platforms offer high-performance query capabilities, scalability, and integration with modern data ecosystems.
Snowflake represents a cloud-agnostic approach with its unique separation of storage and compute resources, while Amazon Redshift is deeply integrated with the AWS ecosystem, offering tight connections to other AWS services.
This comparison reflects the current state of both systems as of 2025, including the latest features and capabilities:
Feature Comparison
Core Database Features
| Feature | Snowflake | Amazon Redshift |
|---|---|---|
| Data Types | Comprehensive set including structured, semi-structured (JSON, XML, Parquet, Avro) | Standard SQL data types, structured data, limited semi-structured support |
| Indexing | Automatic clustering, no manual index management required | Automatic table sort and distribution keys, zone maps |
| Transactions | ACID-compliant with automatic concurrency control | ACID-compliant with serializable isolation |
| Stored Procedures | JavaScript, SQL, Java, Python, Scala | SQL, Python, stored procedures with transaction support |
| Views | Regular, Materialized, Secure | Regular, Late Binding, Materialized |
| Triggers | Limited support through tasks and streams | Limited support, primarily through Lambda integration |
| Partitioning | Automatic micro-partitioning, clustering keys | Distribution keys, sort keys |
| Constraints | Primary key, Foreign key, Unique, Not Null (not enforced) | Primary key, Foreign key, Unique (enforced) |
Advanced Features
| Feature | Snowflake | Amazon Redshift |
|---|---|---|
| High Availability | Built-in redundancy, automatic failover, cross-region replication | Multi-AZ deployments, automatic backups, cross-region snapshots |
| Scalability | Independent scaling of compute and storage, instant scaling | Elastic resize, concurrency scaling, RA3 instances with managed storage |
| Security | Role-based access control, column-level security, row-level security, encryption | IAM integration, VPC, encryption, column-level access control, dynamic data masking |
| Cloud Integration | Multi-cloud (AWS, Azure, GCP), cloud-agnostic | Deep AWS ecosystem integration |
| AI/ML Capabilities | Snowpark for ML, vector search, Cortex AI integration | Amazon Redshift ML, integration with SageMaker, vector search capabilities |
| Extensibility | External functions, UDFs, stored procedures, Snowpark | UDFs, stored procedures, Lambda integration, Apache Spark integration |
Snowflake-Specific Features
- Multi-cloud support (AWS, Azure, GCP)
- Zero-copy cloning for instant data duplication
- Time Travel to access historical data
- Secure data sharing without data movement
- Snowpark for multi-language data processing
- Fully automated optimization (no vacuuming or tuning)
- Unlimited concurrency with isolated warehouses
- SnowGrid for global, cross-cloud connectivity
Amazon Redshift-Specific Features
- Tight AWS integration (S3, Glue, EMR, SageMaker)
- Spectrum for querying S3 data without loading it
- Zero-ETL for seamless data ingestion from AWS sources
- Amazon Q AI-powered SQL assistant
- Auto table optimization and maintenance
- Federated queries across diverse sources
- Serverless option for auto-scaling compute
- Multi-AZ deployments for high availability
Technical Specifications
Architecture
Snowflake Architecture (Cloud-native & Flexible)
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Three main parts:
- Storage: Where all your data lives, stored on cloud platforms like AWS S3, Azure Blob, or Google Cloud Storage.
- Compute: These are virtual warehouses (basically computer power) that process your queries. You can add or remove them anytime.
- Cloud Services: Handles everything else — user logins, tracking metadata, optimizing your queries, etc.
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Key Features:
- Data is automatically organized and optimized in small pieces called micro-partitions.
- Data is stored in columns, which speeds up large analytics queries.
- Storage and compute are separated, so you can scale them independently.
- Multiple compute clusters can run at the same time on the same data — good for teams working in parallel.
Amazon Redshift Architecture (Classic & AWS-Integrated)
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Two main parts:
- Leader Node: Like a manager—it plans and coordinates your query.
- Compute Nodes: Like workers—they store data and do the actual work of running the query.
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Storage:
- Uses Redshift Managed Storage (backed by S3) for scalable storage.
- Data is stored in columns with zone maps to make searches faster.
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How it works:
- Uses Massively Parallel Processing (MPP): data is split into small chunks and processed in parallel across “slices” on the compute nodes.
- You can optimize performance using distribution keys (to control where data goes) and sort keys (to speed up reads).
- Designed to work closely with other AWS services through its internal network.
Query Processing and Performance
Snowflake Query Processing:
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How it works:
- Virtual Warehouses – Like "brain teams" that process queries (you can resize them anytime).
- Auto-Scaling – Adds more power if a query is complex.
- Smart Caching – Remembers results for repeated queries (no extra work needed).
- Self-Optimizing – Automatically adjusts for fastest performance.
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Why it’s easy:
- No manual tuning – Snowflake handles optimizations.
- Isolated workloads – Different teams (warehouses) won’t slow each other down.
Amazon Redshift Query Processing:
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How it works:
- Leader Node – The "boss" that plans and distributes work.
- Compute Nodes – Workers that execute queries in parallel.
- Concurrency Scaling – Adds temporary workers during busy times.
- AQUA (Advanced Query Accelerator) – Special hardware for super-fast queries.
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Why it’s powerful (but needs attention):
- Manual tuning helps (e.g., setting distribution keys).
- Works best when optimized for AWS.
Data Storage and Management
Snowflake Data Storage (Like a Smart, Self-Organizing Warehouse)
- Auto-Partitioning – Splits data into tiny, optimized chunks ("micro-partitions").
- Columnar Storage – Stores data vertically (like a spreadsheet) for fast queries.
- Time Travel – Lets you restore data from any point in time (like undo history).
- Zero-Copy Cloning – Instantly duplicates data without extra storage costs.
- Handles All Data Types – Works with tables (structured) and JSON/Parquet (semi-structured).
- Always Encrypted – Secures data by default.
Best for: Users who want hands-off, auto-optimized storage.
Amazon Redshift Data Storage (Like a High-Speed Factory Floor)
- Redshift Managed Storage (RMS) – Uses S3 for scalable storage behind the scenes.
- Columnar + Compression – Stores data efficiently for fast scans.
- Backups & Snapshots – Automatic backups with point-in-time recovery.
- Distribution Styles – Lets you control how data is spread (for performance tuning).
- Sort Keys – Physically orders data to speed up filtered queries.
- Auto-Maintenance – Runs "vacuum" and "analyze" to keep performance sharp.
- S3 Integration – Easily extends storage to AWS S3.
Best for: AWS-centric teams who want control over data layout.
Development Workflow
Snowflake (Flexible, Cloud-Agnostic Development)
- Snowsight Web UI: A modern, easy-to-use web interface for development and data exploration.
- Dev Tools Support: Works well with tools like VS Code, SnowSQL (CLI), and supports multiple languages (Python, Java, SQL via Snowpark).
- Schema Management: You define your tables and structures using standard SQL, or code with Snowpark.
- Version Control: No built-in Git, but integrates with partners or you manage SQL files in Git manually.
- Deployments: Snowflake supports workflows via tasks and third-party CI/CD tools (like GitHub Actions).
- Testing: You need to rely on custom test frameworks or external tools for testing changes.
- CI/CD: Flexible and works well with various tools, but not deeply tied to any one ecosystem.
Amazon Redshift (AWS-Native, Integrated Workflow)
- Query Editor v2: A good web interface, though not as advanced as Snowsight.
- Tight AWS Integration: Built to work seamlessly with AWS services like AWS Glue (for schema/catalog), CodeCommit (Git), CloudFormation (infra templates), and CodePipeline (CI/CD).
- Schema Management: You can use SQL or AWS Glue for catalog integration.
- Version Control: Uses AWS CodeCommit or other Git tools; integrates easily with AWS build tools.
- Deployments: Can be fully automated using AWS CloudFormation and CodePipeline.
- Testing: Leverages AWS-native DevOps tools or integrates with third-party testing platforms.
- CI/CD: Strong built-in support for building pipelines directly inside AWS.
Pricing and Licensing
Snowflake Pricing (Pay-as-you-go, flexible but complex)
- Licenses: 4 tiers (Standard → Enterprise → Business Critical → VPS).
- Compute: Per-second billing (virtual warehouses scale up/down).
- Storage: Monthly per TB (compressed).
- Cloud Services: Mostly included in compute costs.
Best for: Bursty workloads, multi-cloud users, or teams needing flexible scaling.
Amazon Redshift Pricing (AWS-integrated, discount options)
- Licenses: On-demand, Reserved Instances (1-3 yr discounts), or Serverless.
- Compute: Hourly (node-based) or Serverless (pay per query).
- Storage: Redshift Managed Storage (RMS) per GB.
- Extras: Spectrum (query S3), Concurrency Scaling (extra cost after free tier).
Best for: Steady AWS workloads, teams wanting long-term discounts (Reserved Instances).
Conclusion
When it comes to choosing between Snowflake and Amazon Redshift, Snowflake excels for multi-cloud flexibility, hands-off management, and advanced features like data sharing, while Redshift is ideal for AWS-centric environments with cost-efficient steady workloads and deep AWS integrations.
