Redshift Serverless vs. Provisioned Redshift
The primary difference is the management model and pricing structure. Choosing between them depends on your workload patterns and management preferences.
| Feature | Amazon Redshift Serverless | Amazon Redshift Provisioned |
|---|---|---|
| Management | No clusters to manage. AWS handles all provisioning, scaling, patching, and maintenance. | Self-managed. You must provision and manage clusters, including node types, node count, and scaling policies. |
| Scaling | Automatic. Compute capacity scales up and down seamlessly to meet workload demands. | Manual or Scheduled. Scaling requires manual intervention or pre-configured schedules (e.g., Elastic Resize). |
| Cost Model | Pay-per-use. Billed per second for compute (in RPU-hours) when the data warehouse is active. Storage is billed separately. | Fixed cost. Billed per hour for the provisioned cluster, regardless of whether it is active or idle. Reserved Instances are available for discounts. |
| Performance | Excellent for variable workloads. Performance is managed by setting a base and max compute capacity. | Consistent and predictable performance for steady-state workloads. More granular control over performance tuning. |
| Ideal Use Cases | - Intermittent or unpredictable workloads - Ad-hoc analytics & data exploration - Development and test environments - Variable BI reporting dashboards |
- Steady-state, predictable production workloads - Applications requiring fine-grained control over performance and configuration - Environments where costs must be fixed and predictable |
Rule of Thumb: Choose Serverless for simplicity, variable workloads, and to eliminate management overhead. Choose Provisioned for predictable, high-volume production workloads where you need maximum control and cost predictability.
Core Components
Redshift Serverless separates the concepts of database objects and compute resources.
- Namespace: A logical container for your database objects. It holds all your schemas, tables, views, users, and other data-related settings. You can have multiple namespaces.
- Workgroup: A collection of compute resources that execute your queries. You configure a workgroup with a base capacity and scaling options. A workgroup can only be associated with one namespace at a time, and it's where you define network and security settings.
Compute, Scaling, and Pricing
- Redshift Processing Units (RPUs): Compute capacity is measured in RPUs. One RPU provides 2 vCPU and 16 GB of memory. You configure your workgroup with a base capacity (e.g., 32 RPUs), which determines the baseline performance and cost.
- Automatic Scaling: Redshift Serverless automatically scales the number of RPUs up or down based on query volume and complexity to maintain consistent performance.
- Pay-per-use Pricing:
- Compute: You are charged for the compute capacity used, billed in RPU-hours on a per-second basis (with a 60-second minimum charge). You only pay when queries are active.
- Storage: You pay a standard rate for the amount of primary data stored in Redshift-managed storage.
Key Features & Use Cases
Features
- Simplified Operations: Eliminates the need to manage clusters, allowing data analysts and developers to get started with analytics quickly.
- Intelligent Scaling: Automatically adjusts resources to deliver high performance for demanding and unpredictable workloads.
- Data Lake Integration: Seamlessly integrates with your Amazon S3 data lake via Redshift Spectrum, allowing you to query external data without loading it.
- Full SQL Capabilities: Provides the same powerful Redshift SQL capabilities as the provisioned version.
Use Cases
- Variable Workloads: Perfect for analytics needs that fluctuate, such as BI reports that run hourly or daily.
- Ad-hoc Analysis: Allows data scientists and analysts to run complex queries without worrying about impacting production clusters or managing infrastructure.
- Development & Testing: Provides an easy and cost-effective way to create development or staging environments for Redshift.
- Data Transformation (ETL/ELT): Can be used effectively for data engineering and transformation pipelines with minimal infrastructure setup.
Security & Monitoring
- Security:
- Encryption: Data is encrypted at rest using AWS KMS and in transit using SSL.
- IAM: Access control is managed through AWS Identity and Access Management (IAM) roles and policies.
- VPC: Deploys within a Virtual Private Cloud (VPC) for network isolation and security.
- Monitoring:
- Amazon CloudWatch: Provides key metrics on performance and resource utilization, such as CPU usage, query latency, and storage consumption.
- Query Monitoring: Use the Redshift Console to analyze query performance, identify bottlenecks, and optimize workloads.
- Audit Logging: Logs user activities and database changes for compliance and troubleshooting.
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