What is your primary use case for MongoDB Atlas?

I still have recent experience with MongoDB Atlas as I have a contact with a representative for Brazil. Azure and OCI are what we use as our main cloud providers. I have hands-on experience with OCI, although I don't have a cloud for MongoDB Atlas; I have a cloud for databases and DevOps. I don't develop directly with only MongoDB Atlas. However, I know the organization has a license with the product.

We are using MongoDB Atlas for our log storage, transactional log storage, and we are into CPaaS business, communication platform as a service. We are also using PostgresSQL in some of the applications, alongside MongoDB Atlas.

I primarily use Oracle databases, but I work with many other databases such as MongoDB Atlas ( /products/mongodb-atlas-reviews ) and several cloud databases. I utilize MongoDB Atlas ( /products/mongodb-atlas-reviews ) predominantly for training-level projects in resource grooming and for sub-projects at my office. It is used alongside Oracle and Postgres in these training layers.

I used MongoDB Atlas for structured data storage as part of an application service provided to one of our customers. The application was based on MongoDB and Atlas. While Google Cloud SQL was used for consulting, I interacted with Google Cloud but was not the final decision maker.

The application we are working on is built on MongoDB.

We primarily utilize MongoDB Atlas for tasks such as IoT integration. Additionally, it serves as a general-purpose database that aggregates analytics data before transferring it to a data lake. Its versatility allows for various applications, providing flexibility and ensuring the availability of essential data across different systems. While it is used in diverse contexts, many use it for IoT-related initiatives.

We may use it as an application database. The application stores the data as documents in the database, which is a preference for our company because it’s a Document DB and a NoSQL database, which are preferred over traditional relational databases.

We restore our golden data from various sources and then push it to MongoDB. We make our CDP from MongoDB, which serves as a device-centric system.

In my SaaS role, MongoDB Atlas helped me meet compliance standards by keeping tenant and customer data in separate databases. I improved performance by setting up Atlas clusters in each region. In a high-restriction context, data replication between regions ensured smooth failover without constant snapshots. For added security, I even took data snapshots to another cloud. Choosing MongoDB Atlas depends on your specific SLA and disaster recovery needs.

It's good for performance and stability if you need a non-SQL database to store data.

MongoDB Atlas is a schema-less database.

We use MongoDB Atlas to manage transactions and tenant payments for our application.

I use MongoDB Atlas to take care of my company's clients who are in industries like healthcare systems, finance, and other such areas.

I use the solution for Web Application creation.

MongoDB Atlas can be used to store data, migrate data from SQL to NoSQL, and collect data from websites.

I use the solution for our document databases, cloud databases, e-commerce databases, and invoices.

We work with millions of rows of data and we use MongoDB Atlas for data.

I used this Solution to create a web application with a team using HTML, CSS, JavaScript, React.js, Express, and Node.js. One of these Web applications is Bconnected. It also provides a fully managed, scalable, and flexible database environment. MongoDB Atlas is also well-suited for real-time analytics use cases, such as real-time monitoring of social media feeds. With its ability to support high-throughput read and write operations, This solution can help organizations perform real-time analytics on large volumes of data.

For MongoDB as a service, there are two distinct ways to use it: as a personal user, where one can register on Atlas and experiment with its features; and as a professional, where one can use it for backup management, environment management, and creating figures. Additionally, MongoDB Atlas has features such as data lake capability, the ability to create charts from queries without using other BI tools, and Apache Lucene for text search. I have experimented with these features, but I have not used them professionally. The most relevant use for me is managing backups. Atlas MongoDB also allows for making REST calls and creating applications with triggers, although I have not used it for programming applications much.

We're developing a product using multi-tenant architecture, but we don't have any predefined structure, so we need to use MongoDB Atlas to support predefined architecture.

We use the solution to build applications online.

We create a MongoDB database or clusters. Then, we connect it to the application in a normal simple way.

There are many scenarios that MongoDB can fit into, like where there is a fluid schema and no transaction management. This solution is deployed on a cloud and AWS is the provider.

We use this solution for our NoSQL requirement. We store data, like JSON data, in MongoDB. It's deployed on the cloud with AWS.

I manage services and keep them running. The business analyzes the social media data with machine learning, and that data is stored in MongoDB.

We store a lot of raw data in MongoDB and all our relational data is in RDS Aurora. We are still evaluating Aurora, however, primarily, most raw data is in MariaDB.

We use this solution for database-managed services.

MongoDB can be used for many things. It's a document store, so you can add whatever collection you need to it. We use it for an application that we've built. My team is not using it at this time, although other teams in the company may be.

We use MongoDB Atlas for our MongoDB deployments.

Our use case for MongoDB Atlas is not an as an RDBMS. It is a NoSQL database which means that it's not like a traditional database such as Oracle or Microsoft SQL Server. It's a NoSQL database, and it is much easier to manage. The scalability is very high, the performance is very high, and the cost is lower as compared to the traditional database for cloud.

We use it for all of our temporary data, anything which is not warehoused. It works with AWS Lamda and EC2.

It is one of the main database back-ends for one of our products in the company.

* High availability * Resource recovery

It drives a good portion of our client-facing software utilities.

Any business types where we need instructional data.

We use it for hosting data on the cloud.

I used it for testing.

We use it for patient data.