Amazon DynamoDB vs Azure Cosmos DB: What are the differences?

Introduction

Amazon DynamoDB and Azure Cosmos DB are both popular NoSQL databases that provide scalable and highly available storage solutions. While they share similarities in terms of being managed services and supporting NoSQL data models, there are several key differences between the two.

1. Data Models: Amazon DynamoDB uses a key-value data model, where each item is identified by a primary key. It offers limited support for secondary indices and does not provide native support for document or graph data models. Azure Cosmos DB, on the other hand, supports multiple data models including key-value, document, columnar, and graph. It provides native support for JSON-based document data and allows for flexible schema enforcement.

2. Consistency Models: DynamoDB offers two consistency models: eventual consistency and strong consistency. Eventual consistency provides relaxed guarantees, while strong consistency ensures that all read operations return the most up-to-date data. Cosmos DB provides five well-defined consistency models, ranging from strong consistency to eventual consistency. This allows developers to choose the level of consistency that best suits their application requirements.

3. Global Distribution: DynamoDB supports global tables, which enable data replication across multiple AWS regions for low-latency access. However, configuring global tables can be complex and requires manual management. Cosmos DB, on the other hand, natively supports global distribution and automatic multi-region replication. It allows developers to define their desired consistency model on a per-request basis, facilitating global-scale applications without the need for manual configuration.

4. Query Capabilities: DynamoDB provides a rich set of query capabilities, allowing users to query by the primary key, secondary index, or by using filtered queries. However, it lacks support for complex join operations and requires denormalizing data to achieve efficient queries. Cosmos DB supports SQL-like queries using its SQL API, allowing for more advanced querying capabilities including joins, aggregations, and indexing support out-of-the-box.

5. Pricing Model: DynamoDB pricing is based on a pay-per-request model, where users pay for the number of read and write requests made, along with storage consumption. Additional features like global tables and backup storage are billed separately. Cosmos DB, on the other hand, uses a provisioned throughput model, which offers a fixed amount of throughput capacity that can be scaled up or down as per the workload requirements. Users are billed based on the provisioned throughput capacity and the storage consumed.

6. Integration with Ecosystem: DynamoDB integrates seamlessly with other AWS services, making it a suitable choice for organizations already utilizing the AWS ecosystem. It supports integrations with AWS Lambda, Amazon Kinesis, Amazon Redshift, and more. Cosmos DB also integrates with various Azure services, including Azure Functions, Azure App Service, and Azure Kubernetes Service. It provides native support for change feed, allowing developers to build reactive applications that can react to data changes in real-time.

In summary, while both Amazon DynamoDB and Azure Cosmos DB are powerful NoSQL databases, they have key differences in their data models, consistency models, global distribution capabilities, query capabilities, pricing models, and integration with their respective ecosystems. These differences make them suitable for different use cases and application scenarios.