DynamoDB vs. Cassandra: a Complete Comparison in 2025

Amazon DynamoDB and Apache Cassandra are two leading NoSQL databases for large-scale, high-performance workloads. Though both are NoSQL, they differ in architecture, management, and use cases. This article compares them to help you choose the right one.

Introduction to DynamoDB#

Amazon DynamoDB is a fully managed, serverless NoSQL database service provided by Amazon Web Services (AWS). Launched in 2012, it was inspired by the principles of Amazon's internal Dynamo system and designed to provide seamless scalability, consistent performance, and high availability for applications of any scale.

As a fully managed service, DynamoDB eliminates the administrative burden of hardware provisioning, setup, configuration, replication, software patching, and cluster scaling. It automatically scales to accommodate your workload and maintains consistent performance at any scale.

Introduction to Cassandra#

Apache Cassandra is an open-source, distributed NoSQL database designed to handle large amounts of data across many commodity servers. Originally developed at Facebook in 2008, Cassandra was designed as a synthesis of Amazon's Dynamo distributed storage system and Google's Bigtable data model.

Cassandra features a masterless "ring" architecture that is highly resilient, with no single point of failure. It provides linear scalability, meaning that capacity can be increased simply by adding more nodes to the cluster. Cassandra's design emphasizes high availability and partition tolerance, making it suitable for applications that cannot afford downtime.

Quick Comparison Table#

Detailed Comparison#

Architecture Comparison#

DynamoDB operates as a fully managed service with a proprietary architecture hidden from users. Its key architectural features include:

• Serverless Design: No servers to provision, patch, or manage
• Automatic Partitioning: Data is automatically distributed across partitions
• Managed Replication: Data is automatically replicated across multiple AWS Availability Zones
• Automatic Scaling: Capacity adjusts based on traffic patterns
• SLA-backed Availability: 99.99% availability guaranteed by AWS

DynamoDB's architecture is optimized for simplicity from the user's perspective, with AWS handling all the underlying complexity.

Cassandra employs a peer-to-peer distributed architecture with the following key components:

• Peer-to-Peer Model: All nodes are equal, with no master node
• Gossip Protocol: Nodes communicate state information with each other
• Data Centers and Racks: Logical organization of nodes for optimal data placement
• Manual Scaling: Requires explicit addition of nodes and capacity planning
• Tunable Consistency: Configurable consistency levels for different operations

Cassandra's architecture provides more control and flexibility but requires significant operational expertise to manage effectively.

Data Model Comparison#

DynamoDB supports both key-value and document data models:

• Tables: Collections of items with a primary key
• Items: Individual records (similar to rows in relational databases)
• Attributes: Data elements within items (similar to columns)
• Primary Key: Either simple (partition key only) or composite (partition key + sort key)
• Flexible Schema: Items in the same table can have different attributes

Example of creating a table in DynamoDB using AWS SDK for JavaScript:

const AWS = require('aws-sdk');
AWS.config.update({ region: 'us-west-2' });

const dynamodb = new AWS.DynamoDB();

const params = {
  TableName: 'Users',
  KeySchema: [
    { AttributeName: 'userId', KeyType: 'HASH' },  // Partition key
    { AttributeName: 'email', KeyType: 'RANGE' }   // Sort key
  ],
  AttributeDefinitions: [
    { AttributeName: 'userId', AttributeType: 'S' },
    { AttributeName: 'email', AttributeType: 'S' }
  ],
  ProvisionedThroughput: {
    ReadCapacityUnits: 5,
    WriteCapacityUnits: 5
  }
};

dynamodb.createTable(params, (err, data) => {
  if (err) console.error('Error creating table:', err);
  else console.log('Table created successfully:', data);
});

Cassandra uses a wide-column store data model:

• Keyspaces: Containers for tables (similar to schemas in relational databases)
• Tables: Collections of rows with a defined schema
• Rows: Individual records identified by a primary key
• Columns: Data elements with a name and typed value
• Partition Key: Determines data distribution across nodes
• Clustering Columns: Determine sort order within partitions

Example of creating a table in Cassandra using CQL:

CREATE KEYSPACE users_keyspace
WITH replication = {'class': 'SimpleStrategy', 'replication_factor': 3};

USE users_keyspace;

CREATE TABLE users (
  user_id UUID,
  email TEXT,
  first_name TEXT,
  last_name TEXT,
  created_at TIMESTAMP,
  PRIMARY KEY (user_id, email)
);

Performance and Scalability#

DynamoDB offers predictable performance with single-digit millisecond latency for any scale:

• Automatic Scaling: Handles any amount of traffic without performance degradation
• Global Tables: Provides multi-region replication for global applications
• DAX (DynamoDB Accelerator): In-memory caching for even faster performance
• Performance Predictability: Consistent performance regardless of data size
• Provisioned Capacity: Option to reserve capacity for predictable workloads
• On-Demand Capacity: Option to pay per request for variable workloads

Cassandra is designed for high write throughput and linear scalability:

• Linear Scalability: Performance increases proportionally with additional nodes
• Write Optimization: Architected for high-volume write operations
• Tunable Consistency: Balance between performance and consistency as needed
• Multi-Datacenter Replication: Built-in support for global distribution
• Manual Performance Tuning: Extensive options for optimizing performance
• Compaction Strategies: Different strategies for different workload patterns

Consistency Models#

DynamoDB offers two consistency models:

• Eventually Consistent Reads: Lower latency but may not reflect the most recent write
• Strongly Consistent Reads: Higher latency but guaranteed to reflect the most recent write
• ACID Transactions: Support for transactions across multiple items and tables

Example of specifying consistency in DynamoDB:

const params = {
  TableName: 'Users',
  Key: {
    'userId': { S: '12345' },
    'email': { S: 'user@example.com' }
  },
  ConsistentRead: true  // This makes it a strongly consistent read
};

dynamodb.getItem(params, (err, data) => {
  if (err) console.error('Error:', err);
  else console.log('Item retrieved:', data);
});

Cassandra offers tunable consistency levels:

• Multiple Consistency Levels: ONE, QUORUM, ALL, etc.
• Per-Operation Configuration: Different consistency for different operations
• Lightweight Transactions: Limited to single-partition operations
• Eventual Consistency by Default: Optimized for availability over consistency

Example of specifying consistency in Cassandra:

CONSISTENCY QUORUM;

SELECT * FROM users WHERE user_id = 12345;

CONSISTENCY ONE;

INSERT INTO users (user_id, email, first_name, last_name, created_at)
VALUES (12345, 'user@example.com', 'John', 'Doe', toTimestamp(now()));

Management and Operations#

DynamoDB requires minimal operational overhead:

• No Administration: AWS handles all operational aspects
• Automatic Backups: Point-in-time recovery up to 35 days
• Monitoring: Integrated with Amazon CloudWatch
• Security: IAM integration for access control
• Encryption: Automatic encryption at rest and in transit

Cassandra requires significant operational expertise:

• Installation and Configuration: Manual setup and tuning
• Cluster Management: Node addition, removal, and replacement
• Backup and Recovery: Manual processes for snapshots and restoration
• Monitoring: Requires additional tools and setup
• Security: Manual configuration of authentication and authorization

Use Cases#

DynamoDB Ideal Use Cases

• Serverless Applications: Perfect companion for AWS Lambda
• Mobile and Web Applications: Low-latency data access at scale
• Gaming Applications: Leaderboards, user profiles, session management
• Ad Tech: High-volume, low-latency data processing
• IoT Applications: Device data collection and processing
• Microservices: Scalable data storage for distributed services

Cassandra Ideal Use Cases

• Multi-Datacenter Deployments: Global distribution with local performance
• Time-Series Data: Metrics, sensor data, and event logging
• Large-Scale Data Storage: Petabyte-scale data management
• Write-Heavy Applications: High-throughput write operations
• Hybrid Cloud Environments: Consistent platform across different infrastructures
• Applications Requiring Vendor Independence: Open-source flexibility

Cost Comparison#

DynamoDB offers two pricing models:

• On-Demand Capacity: Pay per request, ideal for variable workloads
• Provisioned Capacity: Reserve capacity for predictable workloads
• Additional Costs: Backup storage, data transfer, global tables, etc.
• Free Tier: Limited free usage for new AWS accounts

Cassandra costs include:

• Infrastructure Costs: Servers, storage, network, etc.
• Operational Costs: Personnel to manage and maintain the cluster
• Potential Support Costs: Enterprise support options
• Licensing: Open-source, but enterprise distributions may have costs

Conclusion#

Both DynamoDB and Cassandra are powerful NoSQL databases designed for high scalability and performance, but they serve different needs:

Choose DynamoDB if:

• You want a fully managed, serverless database with minimal operational overhead
• You're already invested in the AWS ecosystem
• You need automatic scaling without capacity planning
• You prefer a pay-per-use pricing model

Choose Cassandra if:

• You need a vendor-independent, open-source solution
• You require multi-datacenter deployments with fine-grained control
• You have the operational expertise to manage a distributed database
• You need extensive tuning options for specific workload patterns
• You prefer predictable infrastructure costs over usage-based pricing

The right choice depends on your specific requirements, existing infrastructure, team expertise, and long-term strategy.