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.

I find MongoDB Atlas highly scalable and easy to use, with very good support. The pricing is quite scalable and applies to various scenarios, both for smaller and bigger companies. 

MongoDB Atlas has supported our data growth well, and my overall impression is very positive. It is easy to work with and has a reliable support structure. For structured data storage and performance, it provides a comprehensive solution, and the feedback was generally positive.

I am not an expert on what improvements could be made to MongoDB. The service is continually evolving with new features while maintaining reasonable pricing, making it attractive for developers.

I have been using MongoDB Atlas since 2017 and Google Cloud Platform since 2018.

There are no issues mentioned regarding stability. I evaluated MongoDB Atlas as not the best solution for the application in the long term, specifically when the services consolidate themselves.

MongoDB Atlas scales well and supports data growth effectively.

The technical support is very good. I have used them sometimes, even recently, and found the feedback to be spot on our needs.

Neutral

The pricing is quite acceptable and scalable. For our service, it was around 300 to 600 euros per month, which was acceptable for our customers. We could scale up for better performance and scale down when needed.

I highly recommend MongoDB Atlas for both smaller and larger companies. 

It is rated an eight out of ten, depending on the use case. As a document-based database, it is one of the better products on the market.

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.

It's better to use the predicate in Java side to sort. If you are trying to sort in MongoDB, the comparator of Sandal will be discussed. It can be sorted, but if you can do the competitor in Java, sorting using predicates (filtering conditions) and all, it'll be faster. That is what I noticed. For conditioning sorting in MongoDB might be slower, but I haven't verified that. I am doing sorting using predicate in Java.

Another concern:

When I use RoboMongo with MongoDB, it gets delayed and slower when the records are more than one billion. If the records are more than one billion, the document page will see it's all documents. If you have more than a thousand series in your system, it will be difficult to scroll down and get the reserve the directory. I think if they can have some horizontal way of displaying the reports, they can't be answered, but I'm not sure. The tool is providing protected. However, in RoboMongo, it is tough to see that, of course. It's better at one thousand or four thousand since in a single row.

I have experience with this product. I've used MongoDB intermittently since around 2020. It's a deep system. You need to find the data, and sometimes use queries. There's a conversion tool that helps transform static queries into MongoDB 3 format.

In RDBMS, we have an option to put triggers and functions in the database. In MySQL, you have an option to put a function as well as a trigger, but I haven't found that option in MongoDB. I can create functions, but I am not able to create a function trigger there. We have to create, get, update, and delete, which I can do in MySQL and SQL Server also. But in the same way, you can perform in MongoDB. That is the only thing I noticed. 

Other than that, the query that is performing, creating, updating, and deleting everything can be made possible in MongoDB. You cannot create only that trigger in MongoDB. I haven't found anywhere to create that trigger. 

Without triggers, you can't automatically execute actions in response to data changes in MongoDB.

That is the only drawback that I find with MongoDB: creating the trigger. Apart from that, I think everything can be possible. We can put function software into the database, and you can execute the review. But when creating the triggers, you need to perform separate functions for that. 

Scalability in MongoDB always depends upon the configuration. We can configure it accordingly, but it is definitely essential. 

Your hardware system should also ensure the number of resources your application is consuming. For example, in my application, if the unit is more than 400 kits. In a point of time, it will get executed. 

I tested that using JMeter. When I'm doing the amenity services, I always put the 500 resource at a time. At a single point of time, 500 resources will get in that 5, so I just find any issues on that. The client also has not had any issues.

When we are doing any of the microservices, we need to ensure using JMeter. Via JMeter, you can ensure, like, how much on the port of ten, how much in the source can be accessible.

It's a one-click install. Maybe, like, two settings. If you already have MongoDB, five to ten minutes is regarding some MongoDB. The only thing that you should know is the port number and the IP address if you're exposing your application to a third party. I think if you're aware of those risks, you can install it immediately. It's easy if you need to collect that data. You might know five to ten minutes. 

We can install the remote engineer system. I don't think it will be a bigger task, but even if you're configuring for multiple people, you just need to add that particular port number in your system. Otherwise, it won't allow you to log in.

Even if you're using Microsoft authentication, we normally have multiple layers of authentication. So use the command password, and then you will get the notifications, whether you are getting log-in or not. That will take some time. 

Maintenance:

For getting queries only, we put a Java set. From the development perspective, once the database is set up and you configure the URLs, everything works fine. You have 192.138.1.1 URL, it automatically connects to the review if the network is enabled. Then it connects to the review. However, it definitely depends on the bus service we are passing. It should work fine with no issues if the configuration is okay.

That is how we install it. Once we have source, then it's the same network. If it is on the same network, we have a contract, the traffic is there, and the agent works.

If I want to test whether my microservices work fine, I use them again, and they test if my microservices are working fine. Normally, almost all microservices are in a rack server, so you don't get the performance there. I haven't found any issues directly.

If you are looking for a robust system with a lot of security concerns, then you should go for IBM. I'm not saying MongoDB is not 100% secure, but for highly confidential data, I would suggest other solutions. 

However, in MongoDB, you can do filing processes and vertical reports. Everything can be done in MongoDB, but the newest is a relationship. You cannot maintain the referential integrity relationship. You can maintain it, but it will be a little tough.

If you want to maintain the relational database in MongoDB, the resource should be at least a minimum of one and a half years highly exposed in MongoDB. Then only we'll be able to manage that data. Even for new joiners, it is a little tough to explain how the relational database is maintained in MongoDB. 

Overall, I would rate the solution an eight out of ten. 

The application we are working on is built on MongoDB.

MongoDB is a NoSQL tool. We can easily add fields. It provides more flexibility to store data. It is flexible to changes. I have not encountered any performance issues.

Searching and browsing through the collection must be made easier.

I have been using the solution for two years.

The product has been stable so far.

The installation was easy. The deployment took an hour. One person is enough to deploy the tool. It does not require much maintenance.

I am using the free version of the solution.

I have used DynamoDB before. MongoDB’s free version is quite good for our use cases. DynamoDB is expensive.

MongoDB is a very good tool for first-time users. Overall, I rate the solution an eight out of ten.

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.

There is a built-in feature called Autoscaling In MongoDB Atlas. This feature automatically adjusts the configuration of MongoDB based on the volume of users we ingest daily. Autoscaling dynamically scales the resources to accommodate the load when our data flow increases.

The real-time data visible within MongoDB Atlas is not accurate. If they can improve the UI that monitors real-time data. It's more impressive and more attractive. It could be more user-friendly.

I have been using MongoDB Atlas for two years.

The product is pretty stable.

The solution is scalable. Autoscaling supports it.

50 users are using this solution

Whenever we have doubts during configuration, we reach out for assistance. We must upgrade certain parameters in our MongoDB setup, prompting us to contact their support team. They resolve such issues within four to five hours.

The initial setup is not very complex. It is easy to use. It's easy to deploy on MongoDB. We push from GitHub. From there, we specify where the data is restored in MongoDB. We continue to connect. It puts the data and delivers it to Argo City.

The product has a yearly subscription.

We have assigned DevOps for security.

The overview and monitoring part will address this issue, and then we will use it to observe any increasing traffic on our website. We also monitor the rising number of connections due to this traffic. It's quite easy to oversee everything in one place. However, the UI isn't particularly user-friendly.

I've also used it in my previous company and found it handy and easy to configure, including easy capabilities.

We are establishing SLAs that are directly tied to MongoDB. All are interconnected with MongoDB. If MongoDB experiences downtime or RAM or CPU usage spikes significantly, users may encounter difficulties logging in. This reliance on MongoDB can pose challenges for user accessibility, particularly when considering the conferencing tools we use.

Overall, I rate the solution an eight out of ten.

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

The solution is valuable since it works as an unstructured database.

I would like the solution to offer more integration capabilities since it is an area where the solution lacks.

I have been using MongoDB Atlas for a few months.

It is a stable solution. Stability-wise, I rate the solution a seven out of ten.

It is a scalable solution.

Around 30,000 to 40,000 of my company's customers use MongoDB Atlas.

I was not involved much with the technical support offered by MongoDB.

The initial setup of MongoDB Atlas is straightforward unless you don't have to deal with any database-related stuff.

I completed the setup phase of the solution in a week since I have a background in handling databases.

For the deployment phase of the solution to be successful, one needs to have a bit of cloud-based knowledge.

I did not get into the solution's licensing costs since that is taken care of by the customers who want to use the product.

I recommend the solution to those who plan to use it.

I rate the overall solution an eight out of ten.

I use the solution for Web Application creation.

The solution is easily scalable and manageable. Tools can be easily added to the solution.

Customer support needs improvement knowledge-wise.

I have been using the solution for two years.

It is a stable solution. I rate the stability a ten out of ten.

It is a scalable solution. I rate it ten out of ten.

I rate the support an eight out of ten.

The initial setup is straightforward. The deployment takes three hours. I rate the initial setup a ten out of ten.

I have seen a 10% return on investment.

The solution is fairly priced. I rate the pricing a seven out of ten. 

I rate the overall solution an eight out of ten.

I am impressed with the tool's integrations.

The tool's implementation should be made easier.

I have been working with the product for two years.

The solution is stable.

The tool is scalable. My company has more than 40 users for the solution.

We had mailed tech support whenever we encountered issues and they had responded to us.

The solution's deployment can be done in-house.

The tool is free since it's an open-source product.

I would rate the product an eight out of ten.

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.

It has a good easy to use gui and the ability to do most of the management operations under automation

The most useful feature is the management of the backup. I use a managed tool offered by MongoDB to manage an on-prem environment and compare it with the SaaS service and software. The solution is very ready-to-use and it is much simpler to manage backups, which cuts down on the amount of work and stress. However, at least two other features should be mentioned in the current versions. Search integrated with Lucene and the possibility of storing vector data. 

There are some Mongo new features that could be useful for the customers I work with, which are related to migration from on-prem to the cloud. MongoDB is currently working on these features. With the latest version of Mongo, there are new tools that help with migrating. However, currently, only Mongo can use these new features. Soon these migration tools should be released to the public and could really assist with migration also from SQL on-prem environment to Atlas.

I have been using the solution for four years.

The solution is very stable.

I give the scalability a nine out of ten. MongoDB is very easy to scale and with Atlas, it is possible with a few clicks and configurations.

The technical support team is skilled, prepared, and really helpful.

Positive

The initial setup is straightforward. Only one person is required for deployment.

For me, MongoDB Atlas could be expensive as every cloud service because I don't have many other terms of comparison, but I think it is not so expensive for customers. In the end, they may be able to save money rather than buy it on-premise however, on-premise, they do not have access to all the features that Atlas exposes. The costs are similar to having a cloud provider and if we look at the short-term, there is a real saving of money investing in their service instead of making it on-prem in the same scenario.

I give the solution an eight out of ten. I am not familiar with other SQL databases on the cloud. I know that Atlas is quite stable and the service is good, providing customers with all the necessary features to use it as a service. MongoDB Atlas is integrated and available on Google, AWS, and Azure.

I advise people to take advantage of the free courses from MongoDB University that are very well done to gain a general knowledge of MongoDB. Therefore, if someone has no experience with Mongo, they can get great preparation for the MongoDB University course without spending any money.