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Amazon DynamoDB vs Azure Cosmos DB vs Google Cloud Datastore: What are the differences?
Comparison of Amazon DynamoDB, Azure Cosmos DB, and Google Cloud Datastore
Amazon DynamoDB, Azure Cosmos DB, and Google Cloud Datastore are three popular NoSQL databases used for storage and retrieval of data in a scalable and efficient manner. Each of these databases has its unique features and capabilities that cater to different use cases. In this comparison, we will highlight the key differences between Amazon DynamoDB, Azure Cosmos DB, and Google Cloud Datastore in terms of performance, scalability, and pricing.
Data Model: Amazon DynamoDB is a key-value and document database that offers flexible data model options for developers to choose from. Azure Cosmos DB, on the other hand, supports multiple data models including document, key-value, graph, and column-family. Google Cloud Datastore primarily uses a document data model for storing data.
Consistency Levels: Azure Cosmos DB provides tunable consistency levels, allowing users to choose between strong, bounded staleness, session, consistent prefix, and eventual consistency. Amazon DynamoDB offers eventual consistency and strong consistency options. Google Cloud Datastore ensures strong consistency for strongly consistent reads and eventual consistency for all other reads.
Global Distribution: Azure Cosmos DB offers global distribution with multiple consistency models across regions. Amazon DynamoDB supports global tables for replication across multiple AWS Regions. Google Cloud Datastore provides automatic multi-region replication with strong consistency.
Scalability: Amazon DynamoDB scales horizontally by adding more read and write capacity units. Azure Cosmos DB offers horizontal scaling with partitioning based on the data size and throughput. Google Cloud Datastore automatically scales based on the load and stores data in a distributed manner.
Pricing Model: Amazon DynamoDB pricing is based on provisioned throughput capacity, storage, and data transfer. Azure Cosmos DB pricing includes throughput, storage, and data transfer costs. Google Cloud Datastore pricing is based on storage, operations, and network egress.
Secondary Indexes: Amazon DynamoDB supports secondary indexes for querying data efficiently. Azure Cosmos DB offers automatic indexing with support for multiple types of indexes. Google Cloud Datastore provides indexing for properties of entities to enable efficient queries.
In Summary, Amazon DynamoDB, Azure Cosmos DB, and Google Cloud Datastore differ in terms of data model support, consistency levels, global distribution, scalability, pricing model, and secondary index capabilities.
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?
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.
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.
Pros of Amazon DynamoDB
- Predictable performance and cost62
- Scalable56
- Native JSON Support35
- AWS Free Tier21
- Fast7
- No sql3
- To store data3
- Serverless2
- No Stored procedures is GOOD2
- ORM with DynamoDBMapper1
- Elastic Scalability using on-demand mode1
- Elastic Scalability using autoscaling1
- DynamoDB Stream1
Pros of Azure Cosmos DB
- Best-of-breed NoSQL features28
- High scalability22
- Globally distributed15
- Automatic indexing over flexible json data model14
- Tunable consistency10
- Always on with 99.99% availability sla10
- Javascript language integrated transactions and queries7
- Predictable performance6
- High performance5
- Analytics Store5
- Rapid Development2
- No Sql2
- Auto Indexing2
- Ease of use2
Pros of Google Cloud Datastore
- High scalability7
- Serverless2
- Ability to query any property2
- Pay for what you use1
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Cons of Amazon DynamoDB
- Only sequential access for paginate data4
- Scaling1
- Document Limit Size1
Cons of Azure Cosmos DB
- Pricing18
- Poor No SQL query support4
Cons of Google Cloud Datastore
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What is Amazon DynamoDB?
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