MongoDB Enterprise Advanced

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.

On-premises

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.

One of our business units uses MongoDB , and we developed an ETL pipeline that extracts data from MongoDB  and transfers it into our data warehouse.

MongoDB is a NoSQL database that is similar to a document database. It offers flexibility in schema adaptation, allowing us to change the schema and add new data points. Additionally, it scales up easily with low memory requirements. This makes it suitable for our data management needs.

There is room for improvement in integrating MongoDB with agentive AI solutions. While solutions for other databases like SQL or PostgreSQL  already exist, MongoDB requires additional integrations for developing AI solutions.

I have about four years of experience working with MongoDB.

The deployment process was straightforward.

MongoDB is highly stable, and I would rate its stability at nine out of ten.

MongoDB is highly scalable. I would rate its scalability nine out of ten.

We use the open-source version of MongoDB and manage it ourselves, so we have not contacted their technical support.

Neutral

Before using MongoDB, we used IBM DB2 . We switched to MongoDB to develop a composite system that includes both SQL and NoSQL databases.

The initial setup of MongoDB was a straightforward process.

We use the free version of MongoDB, so there are no licensing costs.

Based on my experience, I would recommend MongoDB to others. Its usage depends on specific use cases. MongoDB is suitable for document database needs. I would rate MongoDB as eight or nine out of ten, and I would rate the overall solution the same.

Public Cloud

Other

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.

Neutral

We rated MongoDB a seven out of ten.

Public Cloud

Amazon Web Services (AWS)

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.

On-premises