Atlas-DB vs MongoDB Atlas: What are the differences?
Developers describe Atlas-DB as "Backend for managing dimensional time series data, by Netflix". Atlas was developed by Netflix to manage dimensional time series data for near real-time operational insight. Atlas features in-memory data storage, allowing it to gather and report very large numbers of metrics, very quickly. On the other hand, MongoDB Atlas is detailed as "Deploy and scale a MongoDB cluster in the cloud with just a few clicks". 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.
Atlas-DB can be classified as a tool in the "Database Tools" category, while MongoDB Atlas is grouped under "MongoDB Hosting".
Some of the features offered by Atlas-DB are:
- Manages dimensional time series data
- In-memory data storage
- Captures operational intelligence
On the other hand, MongoDB Atlas provides the following key features:
- Global clusters for world-class applications. Support for 60+ cloud regions across AWS, Azure, & GCP.
- Secure for sensitive data. Built-in security controls and features to meet your existing protocols and compliance standards.
- Designed for developer productivity. Integrated tools to manipulate, visualize, and analyze your data. Execute code in real time in response to data changes.
Atlas-DB is an open source tool with 2.4K GitHub stars and 204 GitHub forks. Here's a link to Atlas-DB's open source repository on GitHub.
What is Atlas-DB?
What is MongoDB Atlas?
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Why do developers choose Atlas-DB?
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What are the cons of using MongoDB Atlas?
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What tools integrate with Atlas-DB?
We went with MongoDB , almost by mistake. I had never used it before, but I knew I wanted the *EAN part of the MEAN stack, so why not go all in. I come from a background of SQL (first MySQL , then PostgreSQL ), so I definitely abused Mongo at first... by trying to turn it into something more relational than it should be. But hey, data is supposed to be relational, so there wasn't really any way to get around that.
There's a lot I love about MongoDB, and a lot I hate. I still don't know if we made the right decision. We've been able to build much quicker, but we also have had some growing pains. We host our databases on MongoDB Atlas , and I can't say enough good things about it. We had tried MongoLab and Compose before it, and with MongoDB Atlas I finally feel like things are in a good place. I don't know if I'd use it for a one-off small project, but for a large product Atlas has given us a ton more control, stability and trust.
When creating small proofs of concept or personal projects with document data models, that require a lot of data storage but don't warrant paying for hosting, I use Atlas because of the 500 MB free tier and ease of setup.
Often paired with AWS Lambda or Google Cloud Functions.
Server application hosted on OpenShift is connecting to MongoDB Atlas to perform database operations.