MongoDB Atlas Reviews

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.

From an operational point of view, there were no costs associated with maintaining the database on my side, and service costs were acceptable from both my side and the customer’s perspective.

We use an application called Fully Factory in the Indian stock market. It works by setting price ranges for stocks. For example, if the Bank of India stock sells between 800 and 810 rupees, you set a range of 800 to 850 rupees. The system prioritizes buyers with the lowest price and highest quantity.

MongoDB's "find first" function quickly locates and blocks the remote and quantity. The client's amount is shown in the record, and then it's processed. We take around 500 records, and the first 100 are processed in a batch. This gets executed and recorded. Developers handle tasks like JP, AR, and AP separately. We update the client's inventory and pass it to a third party. In Microsoft, we use the same client cover to determine the quantity and product details. This is then executed in their API Acondra server system.

So, MongoDB Atlas is used in stock market applications to handle large-scale data processing.

For security reasons, I prefer MongoDB Atlas. It supports role-based access control, so you have an entity for each individual. 

Spring Cloud ensures I have this set with Atlas, and Spring Security is entering the security for me. That's why I feel Spring Security is much better. Even if you expose a public method, it will be exposed via an authentication token.

If you're putting a direct authentication case authentication with their sync of Google token, just put a sync token directly. It will automatically type your method. Even if you expose a public method, it will be exposed via authentication token. Unmasked analytics, you have PeerSpot on or authentication token. It won't get executed.

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

Administering the solution is easy.

Apart from meeting all our high-performance database needs, the major use of MongoDB Atlas is for migrating most of the queries toward the UI part. With the Data API feature, we can now create database queries directly from the UI, which we  build in React and Angular. After creating the queries from the UI, we hit the Data API, and the results are obtained directly, making it the best feature we have come across lately.

The most valuable feature of MongoDB Atlas is that it's on the cloud, so you don't need to set up all your servers. It has the latest features like MongoDB Atlas Data Lake, Data APIs, and high-performance MongoDB on the cloud. It also allows us to visualize data through charts. So, the charts are visible on MongoDB Atlas. In my projects, I found users to be the most useful feature of the solution. We deployed MongoDB Atlas for around 500 users and gave them different authorized roles and permissions, allowing us to create an updated database this year.

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.

We use this solution for our NoSQL requirement. We store data, like JSON data, in MongoDB.

It's deployed on the cloud with AWS.

It's a good solution for NoSQL databases.

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

The most valuable feature of MongoDB Atlas is it's seamless when working with a lot of different systems. Additionally, it is able to adjust the data based on the data being received.

We have a data repository that is floating and MongoDB Atlas allows many data from different sources without the need to convert it.

The solution allows for encryption, quick processing, self-manages, and indexes on the fly.