Our main use cases for MongoDB Enterprise Advanced involve customer-related data and transactions related data; all the customer-facing data will be there. For the project we are using it in telecom. All the customer-related transaction data, their plans, their telecom plans, their consumption, and all those details will be there.

We are using MongoDB Enterprise Advanced in the telecom domain, and we are using this in the customer-facing area. All the customer-related data both for transactions and point of sales data are stored here.

Advanced security features are helpful; our database access security is done through IAM, which is an AWS service, and that IAM is integrated with the security manager with single sign-on process, which is also an AWS service. It's a combination of all that. This was integrated with MongoDB Enterprise Advanced security perspective; that's how we are taking care of the access-related security.

We do use the in-memory storage engine as part of performance improvement; these are already tuned.

It improves the performance depending on the load. When the database receives numerous requests, it has to perform. Those threshold limits we come to know, and then automatically these memory enhancement advanced features are configured so that during high demand periods, memory automatically increases to cater to the incoming advanced requests and volume of requests.

We are using real-time analytics and monitoring in MongoDB Enterprise Advanced; it is integrated. The existing advanced feature is integrated with our business analytics interface. All the various business intelligence reports are generated from that.

The integration between data warehouse could be improved. Nowadays, a lot of data is getting generated, so certain ETL flexible scripts with backend database integrations would be an improvement I could see. I will not be able to clearly say there is currently a deficiency there.

All our DBs are integrated with a backend data warehouse, not necessarily AWS. When third-party data warehouses are integrated, we are seeing some ETL job performance issues. It is a one-off scenario so we have not thoroughly done any troubleshooting on that. It could be platform or third-party related issue. From the AWS standpoint, if robust integration and data warehouse integration specific tools are added in the advanced suite, that would definitely be helpful.

I have been working with MongoDB Enterprise Advanced for almost more than three to four years.

It's pretty much stable; we have not faced any major challenges or difficulties with MongoDB Enterprise Advanced. It is basically transactional from the platform standpoint. Mostly from the product heavy search during those times, which presents capacity planning challenges only, but from the platform standpoint, we have not observed any specific technical issues.

It's pretty much stable; I haven't received any complaints regarding stability.

MongoDB Enterprise Advanced is easy to scale.

We did contact technical support for MongoDB Enterprise Advanced but it goes to a central team. We have received fairly good support whenever we reached out to the technical teams; they were prompt. Once in a while there was a bit of a delay in response but that depends on the technical issue. Overall, we are satisfied with the support.

For MongoDB Enterprise Advanced, the initial setup is straightforward; it is not too complex. It is comfortable to implement.

I would say we see value in money and return on investment with MongoDB Enterprise Advanced.

For a small company, the cost of MongoDB Enterprise Advanced is reasonable, but for heavy data usage, we see a little bit of cost pressure but it's acceptable. I will not be able to elaborate on that right now; we are satisfied with the pricing.

It's good; for first-time users, if somebody is looking for a good guaranteed database for all structured and unstructured data, MongoDB Enterprise Advanced is the way to go. On a scale of one to ten, I rate this solution an eight.

MongoDB does well in being able to access our network devices and keep logs and reporting—that's about it.

I would recommend MongoDB as part of a template if anyone is considering free and open-source templating services such as LibreNMS, but as a standalone, I couldn't advise.

MongoDB has definitely helped us improve our network monitoring and reporting dashboard, so I would say it has impacted our operations positively overall.

I'm not sure about the documentation or the knowledge bases available for MongoDB because I don't interact with it at that level, but I would say it's minimal and could be improved.

I am not experienced with MongoDB enough to know any pain points or areas they could improve.

Nothing else comes to mind at this time that could be improved.

We deployed MongoDB about five years ago and it has been in operation since then.

I was not a part of the initial setup or deployment of MongoDB.

One person was involved with the setup team, and it took just a few days to deploy it.

Overall, on a scale of one to ten, I would rate MongoDB an eight; it's mostly because we're still running a monolithic environment on old hardware, so there are some limitations with read-write access.

At this time, I'm only looking into Cisco or Linux or other solutions out of curiosity about possibly switching to it, but currently all that we use are LibreNMS and Splynx.

From what I know, I would say the initial setup of MongoDB was pretty straightforward.

On LibreNMS, they have a template for setting up the environment that includes all the services, so MongoDB is just part of that template, meaning they weren't really too hands-on with setting up MongoDB itself.

One person was involved with the setup team, and their job title was Network Operations Engineer.

I'm familiar with open-source databases such as MongoDB, and I don't think it's Grafana, but it's similar to Grafana, though I'm trying to think of what it's called.

I'm not entirely sure about the main differences between MongoDB and other open-source databases that I've used.

We haven't really delved too much into looking at comparisons for databases.

MongoDB is not currently supporting our AI-driven projects nor do we use it along with AI at all.

I don't know how MongoDB's document-oriented model has benefited our management processes; that's beyond my expertise.

I don't have experience with QRadar or Auvik or similar products.

I'm familiar with some Linux tools, just things such as smokeping, which we use implemented in our LibreNMS environment.

I'm only an operator, so I don't actually spend a lot of time developing MongoDB, thus I'm not sure what the best features are.

I would rate MongoDB an eight out of ten.

I am not a partner of MongoDB; I am just a customer.

I do not use MongoDB in AI projects; only CosmoDB is used for AI projects, as MongoDB is an old pattern for us, and the new workload in AI is for a new pattern, which is CosmoDB for AI apps.

I would recommend MongoDB because it is a good pattern and a good product for legacy; for us, MongoDB is for legacy databases and legacy apps, and in this scope, it is a good pattern and a stable database engine; however, for new deployments and new applications, CosmoDB is a better engine.

My experience as a partner with Microsoft is very good because we have been a partner for three or four years, and it has been a very good experience.

MongoDB may have advantages over Cosmos DB perhaps in metrics because you can make some dashboards with database metrics, and there are many tools in MongoDB for dashboarding that are perhaps better than CosmoDB.

The dashboards in MongoDB have more functionalities; for example, you can create a dashboard with MongoDB database data, and it is simple to create, such as some sales dashboards, while I do not see this functionality to rapidly create such dashboards in CosmoDB.

While MongoDB is a good product, it is also an expensive product for support, and its scalability is acceptable, but the big problem with MongoDB is the cost.

For security in MongoDB, we work with encrypted databases by default, but we have not contracted the security options in our contract because it is too expensive, so we only implement encrypted databases without the security pack, which is very expensive for us; in security, we are at the first steps, just using encrypted databases.

I think additional features needed in MongoDB include perhaps vector databases, as I think they are not supported right now.

I have been working with MongoDB for five years.

The scalability in MongoDB is limited because we only work with ReplicaSet with two servers, and in comparison, the scalability in CosmoDB is much better than the MongoDB ReplicaSet models; although you can set the auto-provisioning of a node in ReplicaSet, it is very expensive, and we have to work with manual scalability in MongoDB.

The performance of MongoDB is good, especially in a ReplicaSet model, but if you want to pass on to another model, for example, Sharding models, it is very complicated; in ReplicaSet, it is acceptable, but if your workload needs more performance, and you must pass to a Sharding model, it is complicated in MongoDB, whereas in CosmoDB, it is simple.

We have seen a little ROI, and we want to target CosmoDB for this return on investment because it is the better model for this feature; however, with MongoDB, it is difficult to calculate the return on investment, as it is too expensive for our use.

We pay approximately 2,000 euros per month for MongoDB.

This solution receives a rating of 7 out of 10.

We used MongoDB on AWS for a specific project.

We put MongoDB on AWS for a specific project. It's easy to use.

If something is wrong on the cluster, then you need to contact the support team. The stability could be better.

I used MongoDB for about a year.

It's okay. It's acceptable. The stability could be better.

If something is wrong on the cluster, you need to contact the support team. At first, when we were trying to build a cluster.

We rated MongoDB a seven out of ten.

I use MongoDB to connect our backend with the MongoDB database. Once connected, it allows us to store and manage our data efficiently.

It's particularly useful because MongoDB is a document-oriented database, so it doesn't require predefined schema definitions which MySQL does. I've used MongoDB in two to three projects.

The most valuable feature of MongoDB is the predefined functions available when using Node.js. These functions simplify the query process, making it user-friendly and straightforward.

Additionally, MongoDB's flexibility in not requiring a predefined schema makes it adaptable to changes.

Another advantage is the straightforward deployment process, especially when deploying on our own server.

I haven't used MongoDB extensively, so I can't pinpoint a specific area that requires significant improvement at this time.

I've used MongoDB in two to three projects.

I haven't faced any breakdowns or stability issues with MongoDB.

MongoDB is easy to scale up or down, making it flexible for varying data needs.

I did certification in AWS in 2023 and used AWS during that period and had about a month of experience using it for basic deployments. However, there hasn't been any work since.

At the start, connecting to the database via Compass or through a direct ID was a challenge. However, once the procedure is clarified, it becomes straightforward.

I would recommend MongoDB because it is widely used by many organizations. It is beneficial to learn MongoDB as it is a common requirement in various projects and companies.

I would rate MongoDB nine out of ten.

We use MongoDB for handling document-based databases, specifically for sending unemployment forms using Form IO.

In remote areas where technology is not widely available, the forms can be scanned and uploaded, and they are stored as key-value pairs in the MongoDB NoSQL database.

MongoDB allows us to store unstructured data with flexibility. It has enhanced our ability to file unemployment processes for individuals who cannot access the system to create a claimant ID.

The most valuable features of MongoDB include the flexible schema for storing data, its replication capabilities with high availability through a replica set setup, and horizontal scalability using sharding.

There was a need for integrating relational database capabilities, however, MongoDB has introduced a relational converter that allows conversion between SQL and NoSQL.

We migrated to MongoDB back in 2019 during the COVID period.

MongoDB scales horizontally using sharding, which is efficient and enhances performance by reducing load and increasing speed.

We used a traditional SQL database before moving to MongoDB. My exposure to MongoDB's ability to handle unstructured data was compelling.

The initial setup was easy, largely due to my preparation for certification and the support from site reliability engineers and architects.

The implementation was assisted by a site reliability engineer, and I prepared a playbook for guidance.

I am not entirely aware of the exact pricing details, however, MongoDB is a fairly valued product.

MongoDB is an excellent choice for those seeking flexibility in storing unstructured data. Its replication, high availability, and horizontal scaling through sharding make it very valuable. Understanding JSON and gaining certification can greatly aid in leveraging MongoDB effectively.

I'd rate the solution ten out of ten.

Our primary use case is mainly for web applications.

The document-based style is valuable as it allows for easy addition of sub-documents, unlike a relational database. It adds flexibility and facilitates data management. The geospatial index feature is also useful for dealing with latitude and longitude data.

The free tools, like MongoDB Compass, could be enhanced. This is especially relevant for the IDEs or similar tools.

I have been using MongoDB for about ten years or so. I am not certain of the exact years, however, it has been since almost version three.

MongoDB is quite stable. I haven't encountered any application-breaking problems with it. It handles backups well and doesn't have significant disadvantages.

I rate the scalability of MongoDB as eight out of ten. It is used for very large databases and is very useful, although we don't use it much.

MongoDB has tech support and customer support, however, I have not personally contacted them.

I have evaluated RDBMS, MySQL, and Azure SQL previously. MongoDB's advantage is its flexibility as a document database, though it doesn't mean it's better than other databases. It depends on the implementation.

The initial setup is relatively easy, similar to setting up MySQL or other databases.

I am not sure about the return on investment as I don't have knowledge regarding the purchase and related aspects.

MongoDB is free of charge. that said, there is also a paid version. We use both free and paid versions.

I evaluated RDBMS, MySQL, and Azure SQL.

To start with MongoDB, I recommend reading their documentation, as it is quite sufficient.

I'd rate the solution nine out of ten.

I am basically a developer and also a freelancer. I take up a lot of freelance projects for which I use MongoDB. I use it for the database system on my website.

The tool provides some free packages for freshers, which is very good because a lot of beginners or students don't want to spend too much money on it. The tool is also user-friendly. I don't make any connections a lot of the time if I use MongoDB in my project.

I previously encountered some issues with the tool, which included downtime issues. Sometimes, the tool goes down temporarily. There are some stability issues in the product.

There are some problems with the tool's website, and it can get laggy, but otherwise, it is pretty good.

I have been using MongoDB for more than a year. I am just a user of the tool.

The tool works most of the time, but it may go down at times. Stability-wise, I rate the solution a seven out of ten.

The tool's scalability is pretty good. Scalability-wise, I rate the solution an eight out of ten.

MongoDB is pretty popular, and we have a lot of documents and support available for it. The community is pretty big for it. I never faced any problems.

I have a little experience with SQL, but. I have major experience with MongoDB because it is well compared to other tools.

The product's initial setup phase is easy.

The product's deployment phase can be done quickly. In a few minutes, we can create a database, get the APIs, and use it without any issues.

The pricing is normal. Price-wise, the product is not too much expensive.

Though the replication features in the product are pretty good, I don't use them a lot.

I definitely recommend the tool to other people. A lot of startups can use it, and some people can already use it. If some students want to do some project, they can use the tool as its pricing is reasonable. The support and stability of the tool are also okay.

I rate the tool an eight out of ten.

I use MongoDB mainly on the back end. We use it in my company for the database, and I also use it on my side projects.

MongoDB is a NoSQL tool that gives us much freedom to manipulate how the data works.

MongoDB should support TypeScript.

I have been using MongoDB for about two years.

I haven't had any issues with the solution's stability. However, our company faced an issue where the tool was down for two hours.

I rate the solution’s stability an eight out of ten.

MongoDB is a very scalable solution. About 10,000 users are using the solution in our organization.

I would recommend the solution to other users because it's scalable and easy to learn. The solution's documentation can be rigid and hard to understand initially. Users should use an ORM or ODM. They should use Prisma or Mongoose as the starting point for using MongoDB. MongoDB is easier to manipulate. The easier the onboarding, the harder the maintenance.

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