This post was originally published on Practical Cloud.
Read the unabridged version here.

Amazon Web Services (AWS) provides a range of database solutions to cater to various application needs. Two popular options are Amazon Relational Database Service (RDS) and Amazon DynamoDB, a managed NoSQL database. This comparison highlights the key differences between RDS and DynamoDB, offering guidance on when to use each based on specific use cases and requirements.

Overview of AWS RDS

Amazon RDS is a managed relational database service that supports several database engines, including:

• Amazon Aurora
• PostgreSQL
• MySQL
• MariaDB
• Oracle Database
• Microsoft SQL Server

RDS automates administrative tasks such as hardware provisioning, database setup, patching, and backups, allowing developers to focus on their applications.

Overview of Amazon DynamoDB

Amazon DynamoDB is a fully managed NoSQL database service that provides fast and predictable performance with seamless scalability. DynamoDB is designed for applications that require consistent, single-digit millisecond latency at any scale. It is highly available, with built-in support for multi-region and multi-master replication.

Key Differences Between RDS and DynamoDB

Data Model

• RDS: Relational database with a structured schema. Data is organized into tables with predefined columns, and relationships between tables are defined using foreign keys.
• DynamoDB: NoSQL database with a flexible schema. Data is stored in tables, but each item (row) can have a different set of attributes (columns). This allows for more flexible and dynamic data structures.

Query Language

• RDS: Uses SQL (Structured Query Language) for querying and managing data. SQL is a powerful and expressive language that supports complex queries, joins, and transactions.
• DynamoDB: Uses a proprietary query language called PartiQL, which is a SQL-compatible query language for querying DynamoDB tables. While PartiQL supports basic SQL-like queries, it does not support complex joins or transactions as SQL does.

Consistency Model

• RDS: Supports strong consistency by default. Transactions and ACID (Atomicity, Consistency, Isolation, Durability) properties are fully supported, making RDS suitable for applications that require reliable and consistent data integrity.
• DynamoDB: Offers two consistency models:
  • Eventual Consistency: Read operations might not immediately reflect the results of a recently completed write operation.
  • Strong Consistency: Ensures that read operations always reflect the latest write operations.

Scalability

• RDS: Vertical and horizontal scaling:
  • Vertical Scaling: Increase the instance size (CPU, memory) or storage capacity.
  • Horizontal Scaling: Use read replicas for read-heavy workloads.
• DynamoDB: Horizontal scaling by design. DynamoDB automatically partitions data and spreads the load across multiple servers. It can handle virtually unlimited throughput and storage capacity.

Performance

• RDS: Performance depends on the instance type, storage configuration, and database engine tuning. It can handle complex queries and transactions efficiently but might require careful management and optimization.
• DynamoDB: Designed for high performance with low latency at any scale. It is optimized for key-value and document data models, providing consistent performance even under high load.

Use Cases

When to Use RDS

• Transactional Applications: Applications that require ACID transactions, such as financial systems, order management, and inventory systems.
• Complex Queries and Joins: Use cases where complex SQL queries, joins, and data relationships are essential, such as business intelligence and reporting.
• Relational Data Models: Applications that benefit from a structured schema with well-defined relationships between entities, such as CRM systems and content management systems.

When to Use DynamoDB

• High-Throughput and Low-Latency Applications: Applications that require consistent, single-digit millisecond latency and can scale horizontally, such as gaming leaderboards, real-time bidding, and IoT applications.
• Flexible Schema Requirements: Use cases where the data model evolves frequently, and a fixed schema is restrictive, such as user profile stores and content recommendation systems.
• High Availability and Multi-Region Replication: Applications that need to be highly available with seamless multi-region replication, such as global e-commerce platforms and distributed applications.

Cost Considerations

• RDS: Costs are based on instance type, storage, and I/O operations. It can become expensive as the instance size and storage requirements increase, especially for high-availability setups with Multi-AZ deployments and read replicas.
• DynamoDB: Pricing is based on the provisioned throughput (read and write capacity units) and storage used. DynamoDB offers on-demand pricing, which can be cost-effective for workloads with variable traffic patterns.

Management and Maintenance

• RDS: AWS manages the underlying infrastructure, including hardware provisioning, patching, backups, and automated failover. However, database tuning and optimization are the user's responsibility.
• DynamoDB: Fully managed service with minimal maintenance requirements. AWS handles all aspects of infrastructure management, including scaling, replication, and backups.

Detailed Use Cases

RDS Use Cases

1. E-commerce Platforms:

   • RDS is ideal for handling transactional operations, product catalogs, and user orders. The relational model supports complex queries for inventory management and customer relationship management.

2. Enterprise Resource Planning (ERP):

   • ERPs require robust data integrity and complex relationships between entities like suppliers, customers, and inventory. RDS supports the transactional requirements and complex queries needed in ERP systems.

3. Financial Applications:

   • Banking and financial applications need strong consistency and ACID transactions to ensure data accuracy and integrity. RDS's support for transactions and relational models makes it suitable for these applications.

DynamoDB Use Cases

1. Real-Time Data Processing:

   • Applications like real-time bidding, gaming leaderboards, and live event tracking benefit from DynamoDB's low latency and high throughput. The flexible schema allows for rapid iteration and updates to data models.

2. Internet of Things (IoT):

   • IoT applications generate massive amounts of data that need to be ingested and queried quickly. DynamoDB's ability to scale horizontally and handle high write and read rates makes it ideal for IoT data storage and processing.

3. Content and User Personalization:

   • DynamoDB is well-suited for storing user profiles, session data, and personalized content recommendations. Its flexible schema allows for storing diverse attributes and evolving data models without downtime.

Conclusion

Choosing between AWS RDS and Amazon DynamoDB depends on your application's specific requirements:

• Use RDS when you need strong consistency, ACID transactions, complex queries, and a relational data model.
• Use DynamoDB for applications requiring high throughput, low latency, flexible schemas, and horizontal scalability.

Understanding the strengths and limitations of each service will help you make an informed decision and design a robust and efficient database architecture for your application.