MongoDB Enterprise Advanced Reviews

We use this solution to store log files. We have a Cloud telephone product and we use MongoDB to store our calling records. A number of users have access to it. 

We plan to continue using this solution.

MongoDB is relatively fast compared to relational databases. The files are stored in XML format, which is a stable format. 

MongoDB is also great for managing logs.

Databases provide the data and any software can access those databases as per the access protocol — the database itself provides the data.

You can have the same database in multiple geographic locations. You can update it from different locations and the other locations also get updated. In that way, it is a good product. Very good.

You can update the server from another platform (Mongoose) which sits on top of MongoDB and makes it a relational database. The beautiful thing about MongoDB is that it's not a relational database — multiple statements are there so it's easy to access. It serves the purpose for which we are using it.

I suppose it could be a little more secure.

I have been using this solution for a couple of years.

This solution is both scalable and stable.

We are actually using the community version so we manage it ourselves. We have engaged a consultant so if we have any problems, we just sort it out with him.

We were also using MariaDB, but MongoDB is good for certain situations in which MariaDB is not suited for. For example, managing the database of log files is easier with MongoDB.

You don't need many people to install MongoDB or to fine-tune it. You need a database expert if you have any problems, but it is fairly simple.

I didn't install it. I believe my team had to spend some time learning how to handle MongoDB, so they gained some expertise and they started using it. Overall, installation doesn't take longer than one hour.

I would absolutely recommend this solution to others. Overall, on a scale from one to ten, I would give this solution a rating of eight.

We use it for document storage and testing of a proof of concept.

I like the document storage feature. It's pretty simple. I also like the distribution of databases. It's easier to put information about my users.

It would help if MongoDB offered a light solution for small projects. Its become a very big database, and we need a smaller solution for our end-users.

I have been using MongoDB for two years.

MongoDB is stable. It's a good server. If you install perfect hardware tools and follow best practices, it'll be a good solution.

MongoDB is scalable. It's very good.

The initial setup is straightforward. It isn't hard to set up, make a cluster, and distribute.

I would recommend it to potential users who have a big project because MongoDB is good for big projects.

On a scale from one to ten, I would give MongoDB a nine.

We use it just for data storage. I joined this company two months ago, and I am just testing it on my local machine. I haven't used it a lot.

It is easy to set up.

It would be good to have scalability for clusters. For example, if we have three clusters, we should be able to increase to five clusters if required. I am not sure if such a feature is currently there. I hope there is good documentation for this.

I have been using this solution for half a year.

We're just doing something new, and we are still in the testing stages. Based on our experience so far, it is stable.

It is scalable.

I haven't contacted their technical support.

It is easy to set up.

I would rate MongoDB an eight out of ten. It is a good product, but it requires some improvements.

We are a solution provider and we develop applications.

Our primary use of MongoDB is data analytics and it is also used for document management.

MongoDB has a simple data-loading interface. It is not as formal as a traditional database system.

There should be better integration with other databases.

The security should be improved.

We have been working with MongoDB for two or three years.

We have had no issues in terms of stability.

This is a scalable solution that is better for data analysts when compared to a traditional database.

I have used other databases but they were SQL solutions. MongoDB is different because it is a NoSQL solution and the structuring of data is less formal. It has to be formatted internally.

The initial setup is good.

This is an open-source solution.

Most of the features are very nice and MongoDB is a solution that I would recommend. If a company or organization needs a document management platform or is doing marketing analysis then this is a good product.

I would rate this solution a seven out of ten.

Our primary use case of this solution is for schema lists. It's easy to populate the data and to get information for summaries and things like that. The second use case is that there is a mainframe and the upgrades on the mainframe can use your CPU time. As the customer is working on the mixed product, it becomes very costly. Using MongoDB internally allows us to divide as much as we can with it. And there is a service provision that I think is much cheaper than continuing the maintenance of the machine. 

The feature I find most valuable, is that it is easy to use. Even a non-technical person will be able to understand it. I also find the integration with other tools very easy.

The price can always be better. I mean, we are a big corporation so it is quite expensive for us. So perhaps they can improve on the price.

I have been using MongoDB for two years now.

MongoDB is very stable.

The solution can be scaled without any issues.

I have not yet contacted the technical support team of MongoDB, but I have great interactions with the MongoDB side. We have our own technical team in our company that takes care of our issues. 

We have used Snowflake before and I can tell you what the difference is between MongoDB and Snowflake. Snowflake is a totally different type of database. It is basically shot across small units and its solutions are only for the cloud. Your access can be private and it can be fast on the queries. Whereas with MongoDB, it takes much longer than with Snowflake if you want to extract. Snowflake is much faster. It has good analytics capabilities, though.

The initial setup was easy and quite fast. The only problem is the provisioning environment within the cloud. Deploying the MongoDB program doesn't take very long. The whole process of deployment needs only one additional person to do its maintenance and to finalize the deployment faster.  

I will rate this solution a seven out of ten because I like the interface and the integration with other tools. In the next version, perhaps they can modernize the storage options. I think they have a very good reputation, from what I hear from our client comments. The program has speed and it has simplicity. If you want to extract the application, the terms of applicability it is good. And you can use the intelligence within the program. 

When the company started, MongoDB was our primary database.

It offers great flexibility where developers can define any key and assign a value to it. This means that there is very little that one has to plan in terms of designing the schema upfront, so developers enjoy a lot of flexibility. Now that we have more use cases for which NoSQL is not suitable, we are trying to move those workloads out of MongoDB.

MongoDB is extremely developer-friendly because when you are starting, there is very little time needed upfront in terms of planning. Whenever a developer wants to build a certain feature, they simply define a key and a value and that's it.

It is very easy to create an index on a field that you want to have searchable.

All of the documents are stored in JSON format, which gives developers a lot of flexibility.

MongoDB should not be used for reporting, analytics, or number-crunching tasks.

The pricing should be improved because the whole design is around replication of data, so in terms of storage costs, in the long run, it will be expensive. The amount of storage grows very quickly when compared to other databases that store data in normalized form. If there were a way that some data could be partitioned or moved into cold storage then it would be very good.

We have been using MongoDB for about four and a half years.

There are bugs in the system but they are not very significant. We have found a workaround for each of those bugs and we have been running the full-scale production cluster for more than four and a half years. As we haven't had any issues, I would say that it is pretty stable.

This solution is used constantly by both us and our customers, every second of every day.

We are not looking at increasing our usage. Rather, we will be moving some of our workloads off of MongoDB. Ultimately, usage will be at a standstill or perhaps even reduced.

This is a scalable solution. We have close to 100 developers who use it. In addition, our entire business makes use of MongoDB. Everything the customer does makes use of this solution, so I would say that we have at least 100,000 users.

Because we are using the Community Edition, we don't have any support whatsoever.

We did interact with them for MongoDB Atlas, and we are still in contact with them to see if we can take something into production a couple of quarters from now.

We did not use another NoSQL database solution prior to MongoDB.

When we installed MongoDB the initial setup was complex. However, now with Atlas, it is very easy. It took us less than a week to deploy and now, with Atlas, there are a lot of things that you don't need to know that was required four years ago.

I did the original cluster deployment on my own.

We are using the Community Edition of MongoDB. However, we would be happy if the pricing for the full version were more competitive.

We use a lot of different database products and the choice depends on the use case.

With respect to NoSQL, we did not evaluate other vendors because when we implemented this solution four and a half years ago, it was the only scalable NoSQL database. This made it a rather obvious choice for us at the time.

The features that I have looked for are in this solution and we are using an older version. The current cloud-offering, MongoDB Atlas, has even more features. It would be a natural fit for us, but it will not be easy to move because we have a lot of dependencies. We have to update drivers, isolate collections, and take care of other issues before we can switch.

My advice for anybody who is implementing this solution, or any other database, is to take care to plan your indexes because it is extremely important. Spending some time designing the document structure in the initial phase will certainly help you in the long run.

I would also suggest that in terms of sharding, try to think about it as early as possible so that when you are ready to scale, it will certainly help to reduce the workload.

Do not rely on MongoDB for any of the analytics use cases. Aggregation works well but do not use it for your reporting or analytics or number crunching-related tasks.

I would rate this solution a nine out of ten.

We use it for big projects. We have multiple DBs on multiple servers, so we have a good performance for it. Sometimes, we are using cloud systems, like Azure, or VMs.

We are using MongoDB like a warehouse for data that has no relation nor a need to scale.

• The document space is the main feature of it. 
• It has good performance.
• The community is great if you have problem.

You need integration with other tools to run the query in MongoDB.

I have been using this solution for two to three years. I have worked with it on multiple projects.

I did have some issues with putting up the server.

I have not used the technical support. If I get stuck, I search for the answers and will luckily find them.

I recommend the solution for my current company. They have used MongoDB for two projects now. We chose MongoDB because of its community.

The initial setup was easy, not complex.

We used consultants for the deployment. The initial deployment took 20 minutes to half an hour. It didn't take long, as it is very simple.

When you compare MongoDB to other DBs like the SQLOne, they are all the same system, in terms of performance. 

I did a benchmark between SQLBase and MongoDB. The performance and some queries in SQLBase are much better.

I would rate the rate the solution as a seven (out of 10).

We used MongoDB to implement a healthcare application into the Amazon Cloud. We deployed that architecture within the South African public health care sector.

One feature that we found most valuable is that it is completely open source - this was majorly important. Because we worked for a nongovernmental organization we had to work with only open source tools. So that was a big selling point for MongoDB. We also needed a document-based DB to build this FHIR application on top of, MongoDB offers. 

Another major selling point was that they're massively scalable. The fact that unlike relational databases, MongoDB allows a lot more scalability and it was more suited for the type of data that we were storing, which was semi-structured healthcare data. It provided very nicely for the standards that we were working - FHIR - which could be interfaced with JSON and Mongo. It had very good JSON capability and storage. Overall, it was a combination of what we were trying to store and the scalability in terms of being able to store a lot of this information over time.

We were quite happy with the product and the actual use of it. We had no particular problem.

In the future, if they could look into supporting FHIR better. FHIR is a healthcare standard. I don't know what that would mean, but, we had to implement a layer on top of it that implements FHIR. But if MongoDB can look into implementing that would be useful.

The two things that were very important for us were basically performance and compatibility.

I have been using this product for about a year.

It is very stable.

We had issues. It wasn't operationalized yet, but our feeling was that it was easy to set up and easy to operate and very stable. So I would say our compatibility and performance are the two things that came up that I know in the project we had problems with. The rest wasn't at any point an issue.

It's massively scalable. It is very scalable in terms of being able to store a lot of this information over time.

In terms of how many users are using this solution, it was a large database with many objects being pumped into it. But, for our purposes, it was just not necessarily the number of users, but the amount of automation being integrated

It was used by the Provincial department of health, or country. So it was basically the nine provinces in South Africa. And each of them had it. It was all the HIV and TB data for all the departments of health in South Africa. It was big.

I can't recall any issues that our technical team ever had. My feeling was that they were satisfied.

The initial setup was very straightforward. It was a pleasure to work with, for everyone. So setting it up, getting up and running, pumping data into it, and actually looking and querying the data was super simple. We were up and running within an hour. We could literally install and start ingesting information into it from the word go. It was very simple to set up and to have tools to actually query and pump objects into it.

We implemented ourselves.

I would definitely recommend MongoDB. I'm hoping MongoDB will continue to be developed from strength to strength because I think it's an awesome tool. I hope that other products, like DocumentDB, will find a way to work with MongoDB to improve the overall stability of the product. It would be good if other services that host Mongo would become more readily available. It was very useful to actually have a hosted MongoDB set up that is maintained by Mongo Atlas.

I would rate it 9 out of 10 because we had very little issues and it did exactly what we wanted it to do.

MongoDB Atlas was the deployment mechanism we went with and that was hugely helpful for us. MongoDB Atlas is part of the MongoDB suite, I think. It's just a deployment of the Mongo. We also deployed it on Amazon using DocumentDB, but we found that MongoDB Atlas worked better in the end.