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  1. Stackups
  2. Application & Data
  3. Databases
  4. Databases
  5. MongoDB vs MongoDB Atlas

MongoDB vs MongoDB Atlas

OverviewDecisionsComparisonAlternatives

Overview

MongoDB
MongoDB
Stacks96.6K
Followers82.0K
Votes4.1K
GitHub Stars27.7K
Forks5.7K
MongoDB Atlas
MongoDB Atlas
Stacks856
Followers940
Votes34

MongoDB vs MongoDB Atlas: What are the differences?

MongoDB and MongoDB Atlas are both popular options for database management, but they have some key differences that users should be aware of.

  1. Deployment: MongoDB is an open-source, NoSQL database that can be self-hosted on any infrastructure. On the other hand, MongoDB Atlas is a fully managed cloud database service provided by MongoDB. It takes care of the infrastructure and automates many administrative tasks, making it easier to set up and manage a MongoDB database.

  2. Scalability and High Availability: MongoDB allows users to scale their databases horizontally by distributing data across multiple servers. However, managing the scaling process can be complex and time-consuming. MongoDB Atlas, on the other hand, provides built-in scalability and high availability features. It automatically handles load balancing and replication, ensuring that the database remains highly available and can handle increased traffic.

  3. Security: MongoDB provides various security features such as authentication, role-based access control, and encryption of data in transit. MongoDB Atlas adds an extra layer of security by offering features like network isolation, encrypted storage, and continuous backups. These additional security measures help protect the database and ensure data integrity.

  4. Monitoring and Management: MongoDB Atlas provides a user-friendly web-based interface and a comprehensive set of monitoring and management tools. Users can easily view real-time performance metrics, set up alerts, and troubleshoot issues. MongoDB, on the other hand, requires users to set up their own monitoring systems and tools.

  5. Cost: MongoDB is open-source and can be used for free. However, users need to manage and provision their own infrastructure, which can result in additional costs. MongoDB Atlas is a paid service that offers different pricing plans based on the selected features and usage. The cost of MongoDB Atlas includes the infrastructure, management, support, and additional features.

  6. Flexibility: MongoDB allows users to customize their database configuration and set up advanced configurations based on their specific needs. MongoDB Atlas provides a more streamlined and standardized setup, limiting some of the customization options. However, it simplifies the deployment process and reduces the time required to get started with MongoDB.

In Summary, MongoDB is a flexible and customizable database that can be self-hosted, while MongoDB Atlas is a managed cloud database service that automates many administrative tasks and provides additional security and scalability features.

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Advice on MongoDB, MongoDB Atlas

George
George

Student

Mar 18, 2020

Needs adviceonPostgreSQLPostgreSQLPythonPythonDjangoDjango

Hello everyone,

Well, I want to build a large-scale project, but I do not know which ORDBMS to choose. The app should handle real-time operations, not chatting, but things like future scheduling or reminders. It should be also really secure, fast and easy to use. And last but not least, should I use them both. I mean PostgreSQL with Python / Django and MongoDB with Node.js? Or would it be better to use PostgreSQL with Node.js?

*The project is going to use React for the front-end and GraphQL is going to be used for the API.

Thank you all. Any answer or advice would be really helpful!

620k views620k
Comments
Ido
Ido

Mar 6, 2020

Decided

My data was inherently hierarchical, but there was not enough content in each level of the hierarchy to justify a relational DB (SQL) with a one-to-many approach. It was also far easier to share data between the frontend (Angular), backend (Node.js) and DB (MongoDB) as they all pass around JSON natively. This allowed me to skip the translation layer from relational to hierarchical. You do need to think about correct indexes in MongoDB, and make sure the objects have finite size. For instance, an object in your DB shouldn't have a property which is an array that grows over time, without limit. In addition, I did use MySQL for other types of data, such as a catalog of products which (a) has a lot of data, (b) flat and not hierarchical, (c) needed very fast queries.

575k views575k
Comments
Mike
Mike

Mar 20, 2020

Needs advice

We Have thousands of .pdf docs generated from the same form but with lots of variability. We need to extract data from open text and more important - from tables inside the docs. The output of Couchbase/Mongo will be one row per document for backend processing. ADOBE renders the tables in an unusable form.

241k views241k
Comments

Detailed Comparison

MongoDB
MongoDB
MongoDB Atlas
MongoDB Atlas

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.

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.

Flexible data model, expressive query language, secondary indexes, replication, auto-sharding, in-place updates, aggregation, GridFS
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.; Reliable for mission-critical workload. Highly available with distributed fault tolerance and backup options to meet your data recovery objectives.; Built for optimal performance. On-demand scaling, resource optimization tools, and real-time visibility into database performance.
Statistics
GitHub Stars
27.7K
GitHub Stars
-
GitHub Forks
5.7K
GitHub Forks
-
Stacks
96.6K
Stacks
856
Followers
82.0K
Followers
940
Votes
4.1K
Votes
34
Pros & Cons
Pros
  • 829
    Document-oriented storage
  • 594
    No sql
  • 554
    Ease of use
  • 465
    Fast
  • 410
    High performance
Cons
  • 6
    Very slowly for connected models that require joins
  • 3
    Not acid compliant
  • 2
    Proprietary query language
Pros
  • 10
    MongoDB SaaS for and by Mongo, makes it so easy
  • 6
    Amazon VPC peering
  • 4
    Granular role-based access controls
  • 4
    MongoDB atlas is GUItool through you can manage all DB
  • 3
    Built-in data browser

What are some alternatives to MongoDB, MongoDB Atlas?

MySQL

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.

PostgreSQL

PostgreSQL

PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.

Microsoft SQL Server

Microsoft SQL Server

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

SQLite

SQLite

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

Cassandra

Cassandra

Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.

Memcached

Memcached

Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.

MariaDB

MariaDB

Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

RethinkDB

RethinkDB

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.

MongoLab

MongoLab

mLab is the largest cloud MongoDB service in the world, hosting over a half million deployments on AWS, Azure, and Google.

Compose

Compose

Compose makes it easy to spin up multiple open source databases with just one click. Deploy MongoDB for production, take Redis out for a performance test drive, or spin up RethinkDB in development before rolling it out to production.

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