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‎content/blog/snowflake-vs-redshift.md‎

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---
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title: 'Snowflake vs. Redshift: a Complete Comparison in 2025'
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author: Adela
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updated_at: 2025/04/18 18:00
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feature_image: /content/blog/snowflake-vs-redshift/banner.webp
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tags: Comparison
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description: 'An extensive comparison between Snowflake and Redshift on features, architecture, development workflow, operability, licensing and more.'
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---
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<HintBlock type="info">
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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.
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</HintBlock>
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| Update History | Comment |
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| -------------- | ---------------- |
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| 2025/04/18 | Initial version. |
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## Why Comparing Snowflake and Amazon Redshift
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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.
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**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.
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This comparison reflects the current state of both systems as of 2025, including the latest features and capabilities:
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- [Feature Comparison](#feature-comparison)
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- [Technical Specifications](#technical-specifications)
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- [Development Workflow](#development-workflow)
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- [Pricing and Licensing](#pricing-and-licensing)
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- [Conclusion](#conclusion)
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## Feature Comparison
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### Core Database Features
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| Feature | Snowflake | Amazon Redshift |
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| --------------------- | ---------------------------------------------------------------------------------- | --------------------------------------------------------------------- |
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| **Data Types** | Comprehensive set including structured, semi-structured (JSON, XML, Parquet, Avro) | Standard SQL data types, structured data, limited semi-structured support |
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| **Indexing** | Automatic clustering, no manual index management required | Automatic table sort and distribution keys, zone maps |
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| **Transactions** | ACID-compliant with automatic concurrency control | ACID-compliant with serializable isolation |
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| **Stored Procedures** | JavaScript, SQL, Java, Python, Scala | SQL, Python, stored procedures with transaction support |
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| **Views** | Regular, Materialized, Secure | Regular, Late Binding, Materialized |
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| **Triggers** | Limited support through tasks and streams | Limited support, primarily through Lambda integration |
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| **Partitioning** | Automatic micro-partitioning, clustering keys | Distribution keys, sort keys |
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| **Constraints** | Primary key, Foreign key, Unique, Not Null (not enforced) | Primary key, Foreign key, Unique (enforced) |
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### Advanced Features
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| Feature | Snowflake | Amazon Redshift |
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| ---------------------- | ----------------------------------------------------------------------------- | ------------------------------------------------------------------------------ |
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| **High Availability** | Built-in redundancy, automatic failover, cross-region replication | Multi-AZ deployments, automatic backups, cross-region snapshots |
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| **Scalability** | Independent scaling of compute and storage, instant scaling | Elastic resize, concurrency scaling, RA3 instances with managed storage |
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| **Security** | Role-based access control, column-level security, row-level security, encryption | IAM integration, VPC, encryption, column-level access control, dynamic data masking |
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| **Cloud Integration** | Multi-cloud (AWS, Azure, GCP), cloud-agnostic | Deep AWS ecosystem integration |
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| **AI/ML Capabilities** | Snowpark for ML, vector search, Cortex AI integration | Amazon Redshift ML, integration with SageMaker, vector search capabilities |
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| **Extensibility** | External functions, UDFs, stored procedures, Snowpark | UDFs, stored procedures, Lambda integration, Apache Spark integration |
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### Snowflake-Specific Features
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- **Multi-cloud** support (AWS, Azure, GCP)
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- **Zero-copy cloning** for instant data duplication
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- **Time Travel** to access historical data
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- **Secure data sharing** without data movement
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- **Snowpark** for multi-language data processing
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- **Fully automated optimization** (no vacuuming or tuning)
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- **Unlimited concurrency** with isolated warehouses
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- **SnowGrid** for global, cross-cloud connectivity
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### Amazon Redshift-Specific Features
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- **Tight AWS integration** (S3, Glue, EMR, SageMaker)
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- **Spectrum** for querying S3 data without loading it
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- **Zero-ETL** for seamless data ingestion from AWS sources
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- **Amazon Q** AI-powered SQL assistant
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- **Auto table optimization** and maintenance
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- **Federated queries** across diverse sources
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- **Serverless option** for auto-scaling compute
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- **Multi-AZ deployments** for high availability
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## Technical Specifications
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### Architecture
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**Snowflake Architecture (Cloud-native & Flexible)**
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- **Three main parts:**
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1. **Storage:** Where all your data lives, stored on cloud platforms like AWS S3, Azure Blob, or Google Cloud Storage.
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1. **Compute:** These are virtual warehouses (basically computer power) that process your queries. You can add or remove them anytime.
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1. **Cloud Services:** Handles everything else — user logins, tracking metadata, optimizing your queries, etc.
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- **Key Features:**
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- Data is automatically organized and optimized in small pieces called **micro-partitions**.
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- Data is stored in **columns**, which speeds up large analytics queries.
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- **Storage and compute are separated**, so you can scale them independently.
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- **Multiple compute clusters** can run at the same time on the same data — good for teams working in parallel.
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**Amazon Redshift Architecture (Classic & AWS-Integrated)**
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- **Two main parts:**
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1. **Leader Node:** Like a manager—it plans and coordinates your query.
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1. **Compute Nodes:** Like workers—they store data and do the actual work of running the query.
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- **Storage:**
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- Uses **Redshift Managed Storage** (backed by S3) for scalable storage.
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- Data is stored in **columns** with **zone maps** to make searches faster.
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- **How it works:**
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- Uses **Massively Parallel Processing (MPP)**: data is split into small chunks and processed in parallel across “slices” on the compute nodes.
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- You can optimize performance using **distribution keys** (to control where data goes) and **sort keys** (to speed up reads).
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- Designed to work closely with other **AWS services** through its internal network.
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### Query Processing and Performance
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**Snowflake Query Processing:**
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- **How it works:**
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- **Virtual Warehouses** – Like "brain teams" that process queries (you can resize them anytime).
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- **Auto-Scaling** – Adds more power if a query is complex.
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- **Smart Caching** – Remembers results for repeated queries (no extra work needed).
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- **Self-Optimizing** – Automatically adjusts for fastest performance.
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- **Why it’s easy:**
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- No manual tuning – Snowflake handles optimizations.
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- Isolated workloads – Different teams (warehouses) won’t slow each other down.
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**Amazon Redshift Query Processing:**
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- **How it works:**
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- **Leader Node** – The "boss" that plans and distributes work.
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- **Compute Nodes** – Workers that execute queries in parallel.
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- **Concurrency Scaling** – Adds temporary workers during busy times.
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- **AQUA (Advanced Query Accelerator)** – Special hardware for super-fast queries.
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- **Why it’s powerful (but needs attention):**
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- Manual tuning helps (e.g., setting distribution keys).
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- Works best when optimized for AWS.
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### Data Storage and Management
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**Snowflake Data Storage (Like a Smart, Self-Organizing Warehouse)**
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- **Auto-Partitioning** – Splits data into tiny, optimized chunks ("micro-partitions").
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- **Columnar Storage** – Stores data vertically (like a spreadsheet) for fast queries.
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- **Time Travel** – Lets you restore data from any point in time (like undo history).
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- **Zero-Copy Cloning** – Instantly duplicates data without extra storage costs.
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- **Handles All Data Types** – Works with tables (structured) and JSON/Parquet (semi-structured).
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- **Always Encrypted** – Secures data by default.
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Best for: Users who want hands-off, auto-optimized storage.
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**Amazon Redshift Data Storage (Like a High-Speed Factory Floor)**
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- **Redshift Managed Storage (RMS)** – Uses S3 for scalable storage behind the scenes.
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- **Columnar + Compression** – Stores data efficiently for fast scans.
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- **Backups & Snapshots** – Automatic backups with point-in-time recovery.
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- **Distribution Styles** – Lets you control how data is spread (for performance tuning).
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- **Sort Keys** – Physically orders data to speed up filtered queries.
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- **Auto-Maintenance** – Runs "vacuum" and "analyze" to keep performance sharp.
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- **S3 Integration** – Easily extends storage to AWS S3.
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Best for: AWS-centric teams who want control over data layout.
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## Development Workflow
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**Snowflake (Flexible, Cloud-Agnostic Development)**
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- **Snowsight Web UI:** A modern, easy-to-use web interface for development and data exploration.
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- **Dev Tools Support:** Works well with tools like VS Code, SnowSQL (CLI), and supports multiple languages (Python, Java, SQL via Snowpark).
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- **Schema Management:** You define your tables and structures using standard SQL, or code with Snowpark.
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- **Version Control:** No built-in Git, but integrates with partners or you manage SQL files in Git manually.
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- **Deployments**: Snowflake supports workflows via tasks and third-party CI/CD tools (like GitHub Actions).
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- **Testing**: You need to rely on custom test frameworks or external tools for testing changes.
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- **CI/CD**: Flexible and works well with various tools, but not deeply tied to any one ecosystem.
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**Amazon Redshift (AWS-Native, Integrated Workflow)**
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- **Query Editor v2:** A good web interface, though not as advanced as Snowsight.
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- **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).
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- **Schema Management:** You can use SQL or AWS Glue for catalog integration.
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- **Version Control:** Uses AWS CodeCommit or other Git tools; integrates easily with AWS build tools.
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- **Deployments:** Can be fully automated using AWS CloudFormation and CodePipeline.
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- **Testing:** Leverages AWS-native DevOps tools or integrates with third-party testing platforms.
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- **CI/CD:** Strong built-in support for building pipelines directly inside AWS.
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## Pricing and Licensing
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**Snowflake Pricing (Pay-as-you-go, flexible but complex)**
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- **Licenses:** 4 tiers (Standard → Enterprise → Business Critical → VPS).
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- **Compute:** Per-second billing (virtual warehouses scale up/down).
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- **Storage:** Monthly per TB (compressed).
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- **Cloud Services:** Mostly included in compute costs.
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Best for: Bursty workloads, multi-cloud users, or teams needing flexible scaling.
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**Amazon Redshift Pricing (AWS-integrated, discount options)**
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- **Licenses:** On-demand, Reserved Instances (1-3 yr discounts), or Serverless.
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- **Compute:** Hourly (node-based) or Serverless (pay per query).
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- **Storage:** Redshift Managed Storage (RMS) per GB.
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- **Extras:** Spectrum (query S3), Concurrency Scaling (extra cost after free tier).
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Best for: Steady AWS workloads, teams wanting long-term discounts (Reserved Instances).
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## Conclusion
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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.
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## References
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1. [Snowflake Official Documentation](https://docs.snowflake.com/)
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2. [Amazon Redshift Documentation](https://docs.aws.amazon.com/redshift/)
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3. [Snowflake Editions and Pricing](https://www.snowflake.com/pricing/)
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4. [Amazon Redshift Pricing](https://aws.amazon.com/redshift/pricing/)
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5. [Snowflake Architecture Overview](https://docs.snowflake.com/en/user-guide/intro-key-concepts)
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6. [Amazon Redshift Architecture](https://docs.aws.amazon.com/redshift/latest/dg/c_high_level_system_architecture.html)

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