MongoDB Enterprise Advanced Reviews

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.

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.

Positive

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.

A typical use case for MongoDB Enterprise Advanced is mostly for the database, storing our data.

I think the document-based approach is valuable. It's not SQL-based, so it's fairly easy to prototype. However, there is a con to that, which is that you don't have any migration. It's a double-edged sword.

We always use Redis for the in-memory storage engine with MongoDB Enterprise Advanced.

I don't really have a deep dive into it, but I see that MongoDB Enterprise Advanced has RAG and Vertex support. However, we really didn't use it because I think one thing also is that Prisma, the ORM that we use, doesn't have full support for MongoDB Enterprise Advanced. It really falls short of that because many developers are using Prisma instead of Mongoose or their underlying native drivers. I think one point is that their native drivers, or how you query data from their native tools, is just much more difficult.

I have been using MongoDB Enterprise Advanced for over three years.

I had technical support from MongoDB Enterprise Advanced over two years ago. I think they resolved it, but it was very long. We waited for a day or two.

On Atlas, it can take about an hour, but on DigitalOcean, if I do it again, I'd say it's maybe two hours or so for setup.

I do it mostly alone for the installation.

I think it depends on the provider. For example, on DigitalOcean, they have strict routing. You have to whitelist an IP address, or you risk getting DDoS. Then if you're going for the Atlas one that MongoDB Enterprise Advanced provides, it's fairly easy to set up.

I use the cloud version of the product. MongoDB Enterprise Advanced is from MongoDB, though I also use DigitalOcean as a third-party provider.

Maybe it's all more about preferences. I think MongoDB Enterprise Advanced has weak support on the ORM side, particularly if you're using Prisma. I think mostly the focus now on the tools we have is PostgreSQL. MongoDB Enterprise Advanced is not really used that much on our side for production.

It's not really a weak point. It's more of a double-edged sword. If you like document-based management databases and you don't want any migrations, then you go to MongoDB Enterprise Advanced. If you want more secure databases, then you use SQL.

On the account management and the way you secure the connection strings, I have utilized advanced security features with MongoDB Enterprise Advanced.

I think it's very expensive on the Atlas enterprise side. But if you go to other providers of MongoDB Enterprise Advanced, such as DigitalOcean, it may be harder to set up, but they can save you money. My review rating for MongoDB Enterprise Advanced is 8 out of 10.

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.

Neutral

MongoDB is a data storage platform. All our application databases are stored in JSON format, and we also use MongoDB replication.

MongoDB is pretty lightweight and fast on the web because we're using non-structured data. One of the reasons we chose MongoDB is that we don't have to worry about the schema. It is also available in the cloud, and we are going to move to Atlas Cloud for MongoDB. MongoDB has many good replication features and good performance.

Some of the solution's transaction capabilities can be improved. Not much support is available on the transaction side. The size of the document that we can share is also limited. MongoDB also has sharding capabilities, but they are a bit complex. There is some issue with the replication of MongoDB across clusters. We're not able to replicate MongoDB.

I have been using MongoDB for almost five years.

I rate the solution an eight out of ten for stability.

The solution is more suited for small to medium-sized businesses.

I rate the solution’s scalability a seven out of ten.

MongoDB and PostgreSQL are very similar solutions.

The solution’s initial setup is very simple.

We are currently using the on-premises version of MongoDB, but we are moving to the cloud. I would recommend the solution to other users.

Overall, I rate MongoDB an eight 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.

I use MongoDB whenever I want to use non-relational data. I use the solution to make a non-relational database or objected data for my personal project related to social media. MongoDB stores the JSON data, and it's easy for me to understand because I'm a JavaScript Developer.

MongoDB should improve its data loading part. If I update data with MongoDB via the browser, the loading takes a lot of time when I want to use the updated data. The loading is further slowed down if my internet is slow. We use MongoDB Compass to avoid these types of issues.

I have been using MongoDB for four years.

MongoDB is a stable solution, and I haven’t seen any bugs in it.

I always connect the solution using the same method. I recently tried to connect to MongoDB, but I couldn't do so. I contacted the technical support team, and they told us that our server is an Asian server. They told us the issue would be fixed automatically in two or three hours. I waited for the server to be fixed and then resumed work. I did not directly contact the support team. I asked another developer facing the same problem to contact the support team.

The initial setup of the solution is easy for me, but it's not easy for any beginner to learn MongoDB. MongoDB gives us the connection string that I connect to the node to ensure the connection is done. MongoDB sometimes gives us an IP problem if their IP and my IP are not the same. MongoDB uses a private IP, but I can use a global IPL.

Sometimes, beginners can't understand why the database is not working despite them connecting MongoDB the way I showed them. Sometimes, my junior developer comes to me and asks why the database is not working. By default, MongoDB sets a strict IP address. Then, I edit the solution's private IP and change it to our global IP. Using a MongoDB connection string is a bit hard for a beginner, but it gets easier as they practice it two or three times.

MongoDB is a non-relational database that is deployed on the AWS and Azure cloud. I recommend PostgreSQL or MySQL to users who do not want to store data in JSON format. I also recommend MongoDB to users who want to build a project that stores JSON-type data in the database. MongoDB makes it easy for me to read the data from a JSON perspective and fetch the data from the database.

Connecting to MongoDB will be easy if you learn the queries and how they connect or manipulate data. For the first time, you have to learn the solution's queries and data flow to use it. The learning process for MongoDB is easy because it has easy-to-understand documentation. Any beginner can read the documentation and try to use the solution.

Overall, I rate the solution a nine out of ten.

We primarily use MongoDB as a database to handle large volumes of data. It's useful when we need to manage millions of records quickly.

The cloud solution offered by MongoDB, known as Atlas, has been invaluable to us. It provides a good, user-friendly interface. The ease of use and scalability make it stand out compared to relational databases.

Improvements could be made in query optimization, particularly when performing lookups or joining tables. Input functions to restrict the amount of data passed in pipelines would be beneficial for this purpose. Additionally, enhancing the documentation to make it more beginner-friendly is crucial. As someone with seven years of experience with MongoDB, I find the ecosystem intuitive, but newcomers often need help with the documentation. 

We have been using MongoDB for seven years now, and we are currently using the latest version.

The platform has good stability.

We contacted the MongoDB team for assistance whenever we encountered issues, particularly when optimizing queries that took too long. They helped us understand the root cause of the problem and provided us with discounts on certain occasions. For instance, when we accidentally used a larger instance, which resulted in higher costs, they understood the situation and offered us a discount to mitigate the expense.

Positive

We chose MongoDB over other solutions because it can handle large volumes of data and is flexible in data manipulation.

The initial setup process is easy.

The product is affordable, but I don't have direct comparisons with other products as I haven't been involved in the billing process.

The NoSQL structure, particularly the document-based data management, has made data management easier for me. MongoDB's approach to handling data in documents rather than traditional tables has been particularly beneficial.

MongoDB's document-oriented model improves development speed by providing each document with its built-in key or ID, similar to a primary key in relational databases. This inherent ID facilitates faster execution of operations, such as searching for specific documents. Additionally, it allows for creating additional indexes, further enhancing performance. The familiarity of JSON-like structure makes it easy for developers, both front-end and back-end, to work with, leading to quicker development and visualization of data.

I advise others to understand the fundamentals of databases and how they store data. They should start with online videos to grasp the ecosystem, focusing on concepts like data storage. The document-oriented structure with binary objects is crucial for individuals with a programming background.

Several limitations related to querying certain documents can be challenging. They express concerns about the lookup process, where we sometimes need to fetch a large amount of data simultaneously. Additionally, there are limitations regarding the size of documents, which may require restructuring or storing data.

I rate it an eight out of ten.