Amazon DynamoDB vs Azure Cosmos DB vs Google Cloud Bigtable

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Amazon DynamoDB

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Azure Cosmos DB

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Google Cloud Bigtable

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

# Introduction

Key differences between Amazon DynamoDB, Azure Cosmos DB, and Google Cloud Bigtable are outlined below:

1. **Data Structure and Model**: Amazon DynamoDB uses a key-value data model with optional support for secondary indexes, Azure Cosmos DB offers a multi-model approach supporting key-value, document, column-family, graph, and Azure Table storage data models, whereas Google Cloud Bigtable is a wide-column store allowing for high-speed storage and retrieval of structured data.

2. **Consistency and Availability**: DynamoDB provides eventual consistency by default with options for strong consistency, Azure Cosmos DB offers five consistency levels to choose from to achieve the desired trade-off between consistency and availability, and Google Cloud Bigtable guarantees high availability and durability but sacrifices consistency in favor of performance.

3. **Scalability**: DynamoDB scales horizontally by increasing read/write capacity units, Azure Cosmos DB provides global distribution and autoscaling based on workload patterns for seamless scalability, and Google Cloud Bigtable offers automatic sharding for handling large amounts of data by distributing it across multiple nodes.

4. **Pricing and Costs**: DynamoDB pricing is based on provisioned throughput capacity and storage used, Azure Cosmos DB follows a pay-as-you-go model charging for throughput, storage, and data transfer, while Google Cloud Bigtable offers pricing based on the amount of storage used and network egress.

5. **Query Language Support**: Amazon DynamoDB supports queries using a SQL-like syntax through the Query and Scan operations, Azure Cosmos DB allows for querying data using SQL, MongoDB, Gremlin, Table, and Cassandra APIs, and Google Cloud Bigtable offers limited querying capabilities with filters but no full SQL support.

6. **Ecosystem Integration**: DynamoDB integrates seamlessly with other AWS services like Lambda, S3, and IAM for a holistic cloud experience, Azure Cosmos DB integrates with Azure services such as Azure Functions, Logic Apps, and Event Grid for building end-to-end solutions, while Google Cloud Bigtable integrates well with Google Cloud Platform services such as Dataflow, Dataproc, and BigQuery for analytics and processing.

In Summary, Amazon DynamoDB, Azure Cosmos DB, and Google Cloud Bigtable differ in their data models, consistency levels, scalability options, pricing models, query language support, and ecosystem integrations to cater to diverse cloud storage and processing needs.

Advice on Amazon DynamoDB, Azure Cosmos DB, and Google Cloud Bigtable

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 · 108.8K 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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Pros of Amazon DynamoDB
Pros of Azure Cosmos DB
Pros of Google Cloud Bigtable
  • 62
    Predictable performance and cost
  • 56
    Scalable
  • 35
    Native JSON Support
  • 21
    AWS Free Tier
  • 7
    Fast
  • 3
    No sql
  • 3
    To store data
  • 2
    Serverless
  • 2
    No Stored procedures is GOOD
  • 1
    ORM with DynamoDBMapper
  • 1
    Elastic Scalability using on-demand mode
  • 1
    Elastic Scalability using autoscaling
  • 1
    DynamoDB Stream
  • 28
    Best-of-breed NoSQL features
  • 22
    High scalability
  • 15
    Globally distributed
  • 14
    Automatic indexing over flexible json data model
  • 10
    Tunable consistency
  • 10
    Always on with 99.99% availability sla
  • 7
    Javascript language integrated transactions and queries
  • 6
    Predictable performance
  • 5
    High performance
  • 5
    Analytics Store
  • 2
    Rapid Development
  • 2
    No Sql
  • 2
    Auto Indexing
  • 2
    Ease of use
  • 11
    High performance
  • 9
    Fully managed
  • 5
    High scalability

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Cons of Amazon DynamoDB
Cons of Azure Cosmos DB
Cons of Google Cloud Bigtable
  • 4
    Only sequential access for paginate data
  • 1
    Scaling
  • 1
    Document Limit Size
  • 18
    Pricing
  • 4
    Poor No SQL query support
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    What is Amazon DynamoDB?

    With it , you can offload the administrative burden of operating and scaling a highly available distributed database cluster, while paying a low price for only what you use.

    What is Azure Cosmos DB?

    Azure DocumentDB is a fully managed NoSQL database service built for fast and predictable performance, high availability, elastic scaling, global distribution, and ease of development.

    What is Google Cloud Bigtable?

    Google Cloud Bigtable offers you a fast, fully managed, massively scalable NoSQL database service that's ideal for web, mobile, and Internet of Things applications requiring terabytes to petabytes of data. Unlike comparable market offerings, Cloud Bigtable doesn't require you to sacrifice speed, scale, or cost efficiency when your applications grow. Cloud Bigtable has been battle-tested at Google for more than 10 years—it's the database driving major applications such as Google Analytics and Gmail.

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    What companies use Amazon DynamoDB?
    What companies use Azure Cosmos DB?
    What companies use Google Cloud Bigtable?

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    What tools integrate with Amazon DynamoDB?
    What tools integrate with Azure Cosmos DB?
    What tools integrate with Google Cloud Bigtable?

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    What are some alternatives to Amazon DynamoDB, Azure Cosmos DB, and Google Cloud Bigtable?
    Google Cloud Datastore
    Use a managed, NoSQL, schemaless database for storing non-relational data. Cloud Datastore automatically scales as you need it and supports transactions as well as robust, SQL-like queries.
    MongoDB
    MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.
    Amazon SimpleDB
    Developers simply store and query data items via web services requests and Amazon SimpleDB does the rest. Behind the scenes, Amazon SimpleDB creates and manages multiple geographically distributed replicas of your data automatically to enable high availability and data durability. Amazon SimpleDB provides a simple web services interface to create and store multiple data sets, query your data easily, and return the results. Your data is automatically indexed, making it easy to quickly find the information that you need. There is no need to pre-define a schema or change a schema if new data is added later. And scale-out is as simple as creating new domains, rather than building out new servers.
    MySQL
    The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.
    Amazon S3
    Amazon Simple Storage Service provides a fully redundant data storage infrastructure for storing and retrieving any amount of data, at any time, from anywhere on the web
    See all alternatives