DynamoDB vs. Redshift: Choosing the Right AWS Database for Your Workload in 2025

Selecting the right database service on AWS is a critical decision that impacts your application’s performance, scalability, and cost-efficiency. Amazon DynamoDB and Amazon Redshift are two powerful database solutions in the AWS ecosystem, but they serve fundamentally different purposes and workloads.

In this in-depth comparison, we’ll examine DynamoDB and Redshift across crucial dimensions including architecture, performance characteristics, use cases, and pricing. By the end, you’ll understand which service best aligns with your specific requirements.

Service Overviews

What is Amazon DynamoDB?

Amazon DynamoDB is a fully managed, serverless NoSQL database service designed for applications that need consistent, single-digit millisecond response times at any scale. It’s a key-value and document database that delivers fast performance through a distributed architecture with seamless scalability.

Key characteristics:

Database type: NoSQL (key-value and document store)
Consistency model: Eventually consistent by default, with an option for strong consistency
Scaling model: Serverless, automatic horizontal scaling
Ideal for: High-throughput OLTP workloads, real-time applications, and serverless architectures

What is Amazon Redshift?

Amazon Redshift is a fully managed cloud data warehouse service designed for large-scale data analytics and business intelligence. It enables you to run complex analytic queries against petabytes of structured data using familiar SQL-based tools and business intelligence applications.

Key characteristics:

Database type: Columnar data warehouse (based on PostgreSQL)
Consistency model: ACID compliant
Scaling model: Cluster-based with node types optimized for different workloads
Ideal for: OLAP workloads, data warehousing, business intelligence, and complex analytics

Fundamental Architectural Differences

The architectural differences between DynamoDB and Redshift highlight their distinct purposes in the data ecosystem:

DynamoDB Architecture

DynamoDB is built on a distributed system that automatically partitions data and traffic across servers to deliver predictable performance at scale. Its key components include:

Partitioning: Data is automatically distributed across partitions based on the partition key
SSD Storage: All data is stored on SSDs for fast access
Replication: Data is automatically replicated across multiple Availability Zones
Serverless: No servers to provision, patch, or manage

This architecture enables DynamoDB to deliver consistent, low-latency performance for individual record retrieval and simple queries, making it ideal for operational workloads.

Redshift Architecture

Redshift uses a massively parallel processing (MPP) architecture organized around clusters:

Cluster-based: Composed of a leader node and compute nodes
Columnar Storage: Data is stored in columns rather than rows, optimizing for analytics
Data Compression: Advanced compression techniques reduce storage requirements
Query Optimization: Sophisticated query optimizer and execution engine
MPP: Distributes data and query load across all nodes

This architecture is designed for running complex analytical queries on large datasets, where processing time is measured in seconds or minutes rather than milliseconds.

Performance Characteristics

The performance profiles of these services differ significantly:

DynamoDB Performance

Latency: Consistent single-digit millisecond response times for individual operations
Throughput: Virtually unlimited; can handle trillions of requests per day
Scaling: Seamless scaling with no performance degradation
Concurrency: High concurrent operations with no bottlenecks
Operations per second: Can support millions of operations per second

Redshift Performance

Latency: Typically seconds to minutes for complex queries on large datasets
Throughput: Optimized for high throughput of large data sets rather than individual records
Scaling: Can scale up to petabytes of data, but scaling operations may impact performance
Concurrency: Limited by cluster size and workload management settings
Operations per second: Designed for fewer, more complex operations

Query Capabilities

The query capabilities of each service reflect their intended use cases:

DynamoDB Query Capabilities

Primary Access Pattern: Key-based retrieval (get by partition key and optional sort key)
Secondary Access Patterns: Via Global Secondary Indexes and Local Secondary Indexes
Query Language: DynamoDB API or PartiQL (SQL-compatible language with limitations)
Complex Queries: Limited; no joins or complex aggregations natively
Filter Expressions: Can filter results client-side, but still retrieves all matching items first
Transaction Support: Supports ACID transactions for up to 100 items or 4MB of data

Redshift Query Capabilities

Primary Access Pattern: SQL queries across multiple tables
Secondary Access Patterns: Various SQL patterns including joins, window functions, etc.
Query Language: SQL (PostgreSQL dialect with Redshift extensions)
Complex Queries: Excels at complex joins, aggregations, and analytical functions
Filter Expressions: Full SQL WHERE clause support with advanced filtering
Transaction Support: Full ACID transaction support

Use Case Comparison

The stark differences in architecture and performance make each service ideal for specific use cases:

When to Use DynamoDB

DynamoDB excels for:

High-traffic web applications: Social networks, content management, gaming leaderboards
Mobile backend services: User profiles, session management, configuration
Real-time applications: IoT data ingestion, event tracking, messaging
Microservices: API backends, stateless services that need persistent storage
Session state management: Storing web or application session data
High-velocity data capture: Clickstream data, log data, time-series data

Specific examples:

Netflix uses DynamoDB to store metadata about movies and TV shows for quick retrieval
Lyft uses it to store ride information and driver locations for real-time updates
Airbnb uses DynamoDB to store user listings and booking information

When to Use Redshift

Redshift is better suited for:

Data warehousing: Centralizing data from multiple sources for reporting
Business intelligence: Running business reports, dashboards, and KPI tracking
Complex analytics: Finding patterns in large datasets that require complex SQL
Historical data analysis: Analyzing trends over time with large historical datasets
ETL destination: Target for transformed data in extract-transform-load processes
Ad-hoc querying: When business users need to explore data with custom queries

Specific examples:

Nasdaq uses Redshift to analyze market data and detect anomalies
Yelp uses it to analyze user behavior across its platform
Pfizer uses Redshift to analyze research data and clinical trials

Data Modeling Approaches

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The data modeling approaches for these services differ significantly:

DynamoDB Data Modeling

Schema flexibility: Schema-less design; attributes can vary between items
Denormalization: Encouraged to minimize queries (often single-table design)
Access pattern driven: Design starts with identifying access patterns
Indexing: Limited to primary key and up to 20 global secondary indexes
Key design: Critical for performance; partition key design affects throughput
Item size limit: 400KB per item

Best practice in DynamoDB often involves a “single-table design” where multiple entity types are stored in one table with carefully designed keys and indexes.

Redshift Data Modeling

Schema defined: Traditional database schema with tables, columns, and constraints
Normalization: Can use normalized or denormalized models depending on query needs
Star or snowflake schema: Often used for analytical workloads
Distribution styles: Data can be distributed across nodes in different ways
Sort keys: Optimize for query patterns
Column compression: Different compression encodings based on data type
No item size limit: Limited only by overall table size

Redshift data modeling typically involves a dimensional modeling approach with fact and dimension tables.

Scalability and Limits

Each service scales differently and has different limitations:

DynamoDB Scalability

Storage scaling: Unlimited storage capacity
Performance scaling: Automatic partition management for throughput
Scaling model: Horizontal scaling by adding partitions
Scaling triggers: Automatic based on traffic or manual capacity adjustments
Global scaling: Global Tables for multi-region replication
Limits: 400KB per item, but virtually no limits on table size or request throughput

Redshift Scalability

Storage scaling: Up to 8PB with RA3 nodes (as of 2025)
Performance scaling: Add more nodes or upgrade node types
Scaling model: Vertical (larger nodes) and horizontal (more nodes)
Scaling triggers: Manual or automated with Redshift serverless
Global scaling: Limited global access; primarily regional
Limits: Practical limits based on cluster size and workload

Consistency and Durability

Both services provide strong durability guarantees but differ in their consistency models:

DynamoDB Consistency

Read consistency options: Eventually consistent (default) or strongly consistent
Write consistency: Always strongly consistent
Global consistency: Eventual consistency with conflict resolution for Global Tables
Durability: Data replicated across multiple AZs
Transaction support: ACID transactions for operations involving multiple items

Redshift Consistency

Read consistency: Strong consistency for all queries
Write consistency: ACID compliant
Global consistency: N/A (regional service)
Durability: Data replicated within the cluster and automatically backed up to S3
Transaction support: Full ACID transaction support

Pricing Model Comparison

The pricing models reflect the different usage patterns of these services:

DynamoDB Pricing

Capacity modes: On-demand capacity (pay per request) or provisioned capacity
Storage cost: Pay per GB-month stored
Read/Write cost: Pay per read/write request unit or provisioned capacity
Additional costs: Backup storage, data transfer, global tables, streams
Free tier: 25 WCUs, 25 RCUs, 25GB storage available in free tier
Reserved capacity: Available for predictable workloads
Typical monthly cost range: From free to thousands depending on scale

For detailed pricing information, refer to our DynamoDB Pricing Guide.

Redshift Pricing

Compute node cost: Pay for each node-hour consumed
Storage cost: Included in node pricing
Pricing models:
- On-demand: pay per hour with no commitment
- Reserved Instances: 1 or 3-year term for discounts
- Redshift Serverless: pay per RPU-hour (Redshift Processing Unit)
Additional costs: Data transfer, Redshift Spectrum
Free tier: Limited free trial available
Reserved pricing: Up to 75% savings with 3-year commitment
Typical monthly cost range: Hundreds to tens of thousands depending on cluster size

Administration and Management

The operational aspects of these services differ significantly:

DynamoDB Administration

Provisioning: No servers to provision, truly serverless
Maintenance: No maintenance windows or downtime for patches
Monitoring: CloudWatch metrics, DynamoDB contributor insights
Backup: On-demand backups or continuous with point-in-time recovery
Security: Fine-grained access control with IAM, encryption at rest and in transit
Operational overhead: Minimal; focus on capacity management and data modeling

Redshift Administration

Provisioning: Cluster provisioning with node type selection
Maintenance: Maintenance windows for patches and upgrades
Monitoring: CloudWatch metrics, Redshift console, advisor recommendations
Backup: Automated backups to S3, cross-region snapshots
Security: VPC, IAM, encryption, column-level access control
Operational overhead: Moderate; requires query optimization, cluster sizing, maintenance planning

Integration with Other AWS Services

Both services integrate well with the AWS ecosystem but with different service affinities:

DynamoDB Integrations

Lambda: Direct integration for serverless applications
AppSync: For GraphQL APIs backed by DynamoDB
API Gateway: Common pattern for REST APIs
Amplify: Simplified client access for mobile and web apps
Kinesis: Stream processing with DynamoDB as a sink or source via DynamoDB Streams
CloudFormation: Infrastructure as Code provisioning
S3: Export/import data between DynamoDB and S3
Glue: ETL jobs can use DynamoDB as a source or target

Redshift Integrations

S3: Redshift Spectrum for querying data in S3 directly
Glue: ETL jobs using Redshift as source or target
QuickSight: Business intelligence dashboards using Redshift data
SageMaker: Machine learning using data in Redshift
Data Exchange: Share and subscribe to data in Redshift
Lake Formation: Data lake governance for Redshift data
Step Functions: Orchestrate ETL workflows including Redshift queries
CloudFormation: Infrastructure as Code provisioning

Example: When to Use Both Together

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While these services serve different purposes, they often complement each other in a modern data architecture:

Real-world scenario: E-commerce platform

DynamoDB for operational data:
- Customer profiles
- Product catalog
- Shopping carts
- Order processing
- Inventory management
- Session tracking
Redshift for analytical data:
- Sales trends analysis
- Customer behavior analytics
- Inventory forecasting
- Marketing campaign performance
- Financial reporting
- Supplier performance metrics
Data flow between systems:
- Operational data from DynamoDB flows to Redshift via:
  - DynamoDB Streams → Lambda → Kinesis Firehose → S3 → Redshift
  - AWS Glue ETL jobs
  - Custom ETL processes

This architecture provides the best of both worlds: fast, scalable operational processing via DynamoDB and comprehensive analytics via Redshift.

Decision Framework: Choosing Between DynamoDB and Redshift

To choose the right service, ask yourself these key questions:

What is your primary workload type?
- OLTP (Online Transaction Processing): Likely DynamoDB
- OLAP (Online Analytical Processing): Likely Redshift
What response times do you need?
- Milliseconds: DynamoDB
- Seconds to minutes: Redshift
What is your data access pattern?
- Key-based lookups: DynamoDB
- Complex SQL queries: Redshift
What is your data volume and velocity?
- High-velocity writes and reads of individual records: DynamoDB
- Batch processing of large datasets: Redshift
What is your preferred development model?
- Serverless/NoSQL: DynamoDB
- SQL/Data Warehousing: Redshift
What skills does your team have?
- NoSQL/key-value design expertise: DynamoDB
- SQL/relational data modeling expertise: Redshift
What is your budget model?
- Pay per request, highly elastic: DynamoDB
- Predictable cluster costs: Redshift

Conclusion: It’s Not Either/Or

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The choice between DynamoDB and Redshift shouldn’t be viewed as an either/or decision but rather which is more appropriate for specific workloads within your application ecosystem.

DynamoDB excels at operational workloads requiring fast, consistent access to individual records or small groups of related items. Its serverless nature makes it perfect for applications with variable traffic patterns or those that need to scale from zero to massive scale instantly.

Redshift shines for analytical workloads where you need to process large volumes of data with complex queries. It’s designed to answer business questions across broad datasets rather than serve individual transactions.

For many organizations, the optimal solution includes both: DynamoDB for operational data and Redshift for analytics. This pattern has become a standard architecture in AWS environments, allowing each service to do what it does best.

When designing your data architecture, focus on the specific requirements of each workload rather than forcing all data needs into a single service. By leveraging the right tool for each job, you can build a more efficient, scalable, and cost-effective system.

For managing your DynamoDB environment more effectively, consider using Dynomate, which provides an intuitive interface for administration, query building, and performance monitoring across your tables.

What’s your experience with DynamoDB and Redshift? Are you using them separately or together in your architecture? Share your insights in the comments below!