Azure Cosmos DB vs Google Cloud Bigtable

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Azure Cosmos DB

510
948
+ 1
129
Google Cloud Bigtable

122
326
+ 1
25
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Azure Cosmos DB vs Google Cloud Bigtable: What are the differences?

Azure Cosmos DB: A fully-managed, globally distributed NoSQL database service. 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; Google Cloud Bigtable: The same database that powers Google Search, Gmail and Analytics. 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.

Azure Cosmos DB and Google Cloud Bigtable can be primarily classified as "NoSQL Database as a Service" tools.

Some of the features offered by Azure Cosmos DB are:

  • Fully managed with 99.99% Availability SLA
  • Elastically and highly scalable (both throughput and storage)
  • Predictable low latency: <10ms @ P99 reads and <15ms @ P99 fully-indexed writes

On the other hand, Google Cloud Bigtable provides the following key features:

  • Unmatched Performance: Single-digit millisecond latency and over 2X the performance per dollar of unmanaged NoSQL alternatives.
  • Open Source Interface: Because Cloud Bigtable is accessed through the HBase API, it is natively integrated with much of the existing big data and Hadoop ecosystem and supports Google’s big data products. Additionally, data can be imported from or exported to existing HBase clusters through simple bulk ingestion tools using industry-standard formats.
  • Low Cost: By providing a fully managed service and exceptional efficiency, Cloud Bigtable’s total cost of ownership is less than half the cost of its direct competition.

"Best-of-breed NoSQL features" is the primary reason why developers consider Azure Cosmos DB over the competitors, whereas "High performance" was stated as the key factor in picking Google Cloud Bigtable.

Microsoft, Rumble, and Property With Potential are some of the popular companies that use Azure Cosmos DB, whereas Google Cloud Bigtable is used by Spotify, Resultados Digitais, and Rainist. Azure Cosmos DB has a broader approval, being mentioned in 24 company stacks & 23 developers stacks; compared to Google Cloud Bigtable, which is listed in 17 company stacks and 3 developer stacks.

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Pros of Azure Cosmos DB
Pros of Google Cloud Bigtable
  • 28
    Best-of-breed NoSQL features
  • 21
    High scalability
  • 15
    Globally distributed
  • 14
    Automatic indexing over flexible json data model
  • 10
    Always on with 99.99% availability sla
  • 10
    Tunable consistency
  • 7
    Javascript language integrated transactions and queries
  • 6
    Predictable performance
  • 5
    Analytics Store
  • 5
    High performance
  • 2
    Auto Indexing
  • 2
    Rapid Development
  • 2
    Ease of use
  • 2
    No Sql
  • 11
    High performance
  • 9
    Fully managed
  • 5
    High scalability

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Cons of Azure Cosmos DB
Cons of Google Cloud Bigtable
  • 17
    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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    Jobs that mention Azure Cosmos DB and Google Cloud Bigtable as a desired skillset
    CBRE
    United States of America Texas Richardson
    CBRE
    United States of America Texas Richardson
    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