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.
What is Google Cloud Bigtable?
What is MongoDB?
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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.
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.
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.
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.