Azure Cosmos DB vs Google Cloud Bigtable

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Azure Cosmos DB

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Google Cloud Bigtable

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

Key Differences between Azure Cosmos DB and Google Cloud Bigtable

Azure Cosmos DB and Google Cloud Bigtable are two popular NoSQL databases that offer different features and capabilities. Here are the key differences between them:

  1. Data Model: Azure Cosmos DB uses a multi-model database approach, allowing developers to choose from a variety of data models including document, key-value, columnar, graph, and time-series. On the other hand, Google Cloud Bigtable is a wide-column store that is optimized for storing large amounts of data in a sparse table format.

  2. Scalability: Azure Cosmos DB is designed to provide global scalability out of the box, with the ability to replicate data across multiple regions for high availability and low latency access. It offers built-in horizontal scaling and automatic partitioning of data. In contrast, Google Cloud Bigtable is also scalable but requires manual sharding to distribute data across multiple instances.

  3. Consistency Models: Azure Cosmos DB supports multiple consistency models, including strong, bounded staleness, session, consistent prefix, and eventual consistency. This allows developers to choose the appropriate consistency level based on their application requirements. Google Cloud Bigtable, on the other hand, only provides eventual consistency.

  4. Query Language: Azure Cosmos DB supports SQL-like queries using its SQL API, as well as MongoDB's query language, Cassandra Query Language (CQL), and Gremlin (a graph traversal language). This provides flexibility for developers to write complex queries using familiar syntax. In contrast, Google Cloud Bigtable does not support SQL-like queries and requires developers to use its low-level API for data access.

  5. Managed Service: Azure Cosmos DB is a fully managed database service, providing automatic backups, patching, and automatic scaling. It also offers global distribution capabilities for low latency access. On the other hand, Google Cloud Bigtable requires more manual configuration and management, such as setting up and managing instances, clusters, and backups.

  6. Integration with Other Services: Azure Cosmos DB integrates seamlessly with other Azure services, such as Azure Functions, Azure Logic Apps, and Azure Event Grid. This enables developers to build end-to-end solutions using a wide range of services. Google Cloud Bigtable integrates well with other Google Cloud Platform services, such as BigQuery, Dataflow, and Pub/Sub.

In Summary, Azure Cosmos DB offers a multi-model database approach with global scalability, multiple consistency models, and strong integration with other Azure services. Google Cloud Bigtable is a wide-column store optimized for large-scale data storage, but requires more manual configuration and management.

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Pros of Azure Cosmos DB
Pros of Google Cloud Bigtable
  • 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 Azure Cosmos DB
Cons of Google Cloud Bigtable
  • 18
    Pricing
  • 4
    Poor No SQL query support
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    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 Azure Cosmos DB?
    What companies use Google Cloud Bigtable?
    See which teams inside your own company are using Azure Cosmos DB or Google Cloud Bigtable.
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    What tools integrate with Azure Cosmos DB?
    What tools integrate with Google Cloud Bigtable?

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    What are some alternatives to Azure Cosmos DB and Google Cloud Bigtable?
    Azure SQL Database
    It is the intelligent, scalable, cloud database service that provides the broadest SQL Server engine compatibility and up to a 212% return on investment. It is a database service that can quickly and efficiently scale to meet demand, is automatically highly available, and supports a variety of third party software.
    MongoDB Atlas
    MongoDB Atlas is a global cloud database service built and run by the team behind MongoDB. Enjoy the flexibility and scalability of a document database, with the ease and automation of a fully managed service on your preferred cloud.
    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.
    Neo4j
    Neo4j stores data in nodes connected by directed, typed relationships with properties on both, also known as a Property Graph. It is a high performance graph store with all the features expected of a mature and robust database, like a friendly query language and ACID transactions.
    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.
    See all alternatives