Amazon DynamoDB vs Azure Cosmos DB

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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.

Advice on Amazon DynamoDB and Azure Cosmos DB

We are building a social media app, where users will post images, like their post, and make friends based on their interest. We are currently using Cloud Firestore and Firebase Realtime Database. We are looking for another database like Amazon DynamoDB; how much this decision can be efficient in terms of pricing and overhead?

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Replies (1)
William Frank
Data Science and Engineering at GeistM · | 2 upvotes · 107.1K views
Recommends

Hi, Akash,

I wouldn't make this decision without lots more information. Cloud Firestore has a much richer metamodel (document-oriented) than Dynamo (key-value), and Dynamo seems to be particularly restrictive. That is why it is so fast. There are many needs in most applications to get lightning access to the members of a set, one set at a time. Dynamo DB is a great choice. But, social media applications generally need to be able to make long traverses across a graph. While you can make almost any metamodel act like another one, with your own custom layers on top of it, or just by writing a lot more code, it's a long way around to do that with simple key-value sets. It's hard enough to traverse across networks of collections in a document-oriented database. So, if you are moving, I think a graph-oriented database like Amazon Neptune, or, if you might want built-in reasoning, Allegro or Ontotext, would take the least programming, which is where the most cost and bugs can be avoided. Also, managed systems are also less costly in terms of people's time and system errors. It's easier to measure the costs of managed systems, so they are often seen as more costly.

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Decisions about Amazon DynamoDB and Azure Cosmos DB
Eduardo Fernandez
Software Engineer at Parrot Software, Inc. · | 5 upvotes · 12K views

CouchDB has proven us to be a reliable multi-master NoSQL JSON database built natively for the web.

We decided to use it over alternatives such as Firebase due topology, costs and frontend architecture.

Thanks to CouchDB we are now a frontend first CRM platform. We are capable of delivering and leveraging our frontend code to build most of our new functionalities directly within the frontend which we enrich through backend sidecars connected to each Parrot and each CouchDB.

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