Google Cloud Bigtable vs MongoDB

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Google Cloud Bigtable
Google Cloud Bigtable

60
49
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MongoDB
MongoDB

16K
12.5K
+ 1
3.8K
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Google Cloud Bigtable vs MongoDB: What are the differences?

Developers describe Google Cloud Bigtable as "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. On the other hand, MongoDB is detailed as "The database for giant ideas". 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.

Google Cloud Bigtable and MongoDB are primarily classified as "NoSQL Database as a Service" and "Databases" tools respectively.

"High performance" is the primary reason why developers consider Google Cloud Bigtable over the competitors, whereas "Document-oriented storage" was stated as the key factor in picking MongoDB.

MongoDB is an open source tool with 16.3K GitHub stars and 4.1K GitHub forks. Here's a link to MongoDB's open source repository on GitHub.

Uber Technologies, Lyft, and Codecademy are some of the popular companies that use MongoDB, whereas Google Cloud Bigtable is used by Spotify, Rainist, and Resultados Digitais. MongoDB has a broader approval, being mentioned in 2189 company stacks & 2218 developers stacks; compared to Google Cloud Bigtable, which is listed in 17 company stacks and 3 developer stacks.

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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.

What is 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.
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    What are some alternatives to Google Cloud Bigtable and MongoDB?
    Microsoft Access
    It is an easy-to-use tool for creating business applications, from templates or from scratch. With its rich and intuitive design tools, it can help you create appealing and highly functional applications in a minimal amount of time.
    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.
    Google Cloud Spanner
    It is a globally distributed database service that gives developers a production-ready storage solution. It provides key features such as global transactions, strongly consistent reads, and automatic multi-site replication and failover.
    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.
    Cloud Firestore
    Cloud Firestore is a NoSQL document database that lets you easily store, sync, and query data for your mobile and web apps - at global scale.
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    Decisions about Google Cloud Bigtable and MongoDB
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    How developers use Google Cloud Bigtable and MongoDB
    Avatar of Tarun Singh
    Tarun Singh uses MongoDBMongoDB

    Used MongoDB as primary database. It holds trip data of NYC taxis for the year 2013. It is a huge dataset and it's primary feature is geo coordinates with pickup and drop off locations. Also used MongoDB's map reduce to process this large dataset for aggregation. This aggregated result was then used to show visualizations.

    Avatar of Trello
    Trello uses MongoDBMongoDB

    MongoDB fills our more traditional database needs. We knew we wanted Trello to be blisteringly fast. One of the coolest and most performance-obsessed teams we know is our next-door neighbor and sister company StackExchange. Talking to their dev lead David at lunch one day, I learned that even though they use SQL Server for data storage, they actually primarily store a lot of their data in a denormalized format for performance, and normalize only when they need to.

    Avatar of Foursquare
    Foursquare uses MongoDBMongoDB

    Nearly all of our backend storage is on MongoDB. This has also worked out pretty well. It's enabled us to scale up faster/easier than if we had rolled our own solution on top of PostgreSQL (which we were using previously). There have been a few roadbumps along the way, but the team at 10gen has been a big help with thing.

    Avatar of AngeloR
    AngeloR uses MongoDBMongoDB

    We are testing out MongoDB at the moment. Currently we are only using a small EC2 setup for a delayed job queue backed by agenda. If it works out well we might look to see where it could become a primary document storage engine for us.

    Avatar of Matt Welke
    Matt Welke uses MongoDBMongoDB

    Used for proofs of concept and personal projects with a document data model, especially with need for strong geographic queries. Often not chosen in long term apps due to chance data model can end up relational as needs develop.

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