Have managed customer transaction data efficiently and supported high-demand workloads with reliable performance
What is our primary use case?
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.
What is most valuable?
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.
What needs improvement?
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.
For how long have I used the solution?
I have been working with MongoDB Enterprise Advanced for almost more than three to four years.
What do I think about the stability of the solution?
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.
What do I think about the scalability of the solution?
MongoDB Enterprise Advanced is easy to scale.
How are customer service and support?
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.
How would you rate customer service and support?
How was the initial setup?
For MongoDB Enterprise Advanced, the initial setup is straightforward; it is not too complex. It is comfortable to implement.
What was our ROI?
I would say we see value in money and return on investment with MongoDB Enterprise Advanced.
What's my experience with pricing, setup cost, and licensing?
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.
What other advice do I have?
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.
Which deployment model are you using for this solution?
Hybrid Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Amazon Web Services (AWS)
2+ Yrs of experience using mongodb atlas
What do you like best about the product?
Flexibility, Ease of use and efficiency as well database connection and interaction while development phase.
What do you dislike about the product?
For the NoSQL databases, it's fine; I didn't feel that anything was wrong.
What problems is the product solving and how is that benefiting you?
We have used this database for the IOT and edtech domain projects. The flexibility provided by the database is insane for the IOT application there is irrelevant and most of the unstructured data we are storing, its critical to manage in sql databases
A review by the user having experience of 2+ years
What do you like best about the product?
simplicity, ease of use, scaling, sharding, data management
What do you dislike about the product?
Sometimes the database clusters take time while loading the data
What problems is the product solving and how is that benefiting you?
We have used MongoDB Atlas to build IoT applications as well as payment systems.
Best cloud database management system for nosql db
What do you like best about the product?
By using the mongodb atlas cloud it is very easy to scale up on one click and it is very easy to integrate with application and for the day to day usage it is very easy .It is customer support and user interface is soo easy to manage.
What do you dislike about the product?
In dont like about the altas is that that pricing comes expensive as a data and cluster. cold start issue for the paused cluster.
What problems is the product solving and how is that benefiting you?
It is remove the complexity of the database server and the mangement and one best thing about it that it provides the automatic backups and high availablity for the poducation grade system this features benifit use alot.
Mongodb as the good flexibility, scalable to more data and performance , stores different data types
What do you like best about the product?
It good as it store different types of data structures, different types of documents as it as good scalability and has good performance.
What do you dislike about the product?
It doesn't support multi document ACID and contains high memory usage which has data inconsistencies sometimes.
What problems is the product solving and how is that benefiting you?
It helped me in doing the complex projects with the multiple documents and is flexible and has high availability and performance.
Open-source tool improves network monitoring and reporting efficiency
What is our primary use case?
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.
What is most valuable?
MongoDB has definitely helped us improve our network monitoring and reporting dashboard, so I would say it has impacted our operations positively overall.
What needs improvement?
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.
For how long have I used the solution?
We deployed MongoDB about five years ago and it has been in operation since then.
What was my experience with deployment of the solution?
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.
What do I think about the scalability of the solution?
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.
Which solution did I use previously and why did I switch?
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.
How was the initial setup?
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.
What about the implementation team?
One person was involved with the setup team, and their job title was Network Operations Engineer.
Which other solutions did I evaluate?
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.
What other advice do I have?
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.
Which deployment model are you using for this solution?
On-premises
Offers reliable engine for legacy needs but requires enhanced cost management and AI features
What is our primary use case?
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.
What is most valuable?
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.
What needs improvement?
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.
For how long have I used the solution?
I have been working with MongoDB for five years.
What do I think about the scalability of the solution?
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.
What was our ROI?
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.
What's my experience with pricing, setup cost, and licensing?
We pay approximately 2,000 euros per month for MongoDB.
What other advice do I have?
This solution receives a rating of 7 out of 10.
Friendly to use of collections
What do you like best about the product?
The mongodb GUI is very good to view the collection and manage to the databases
What do you dislike about the product?
The GUI of we cannot see the user details
What problems is the product solving and how is that benefiting you?
Easy to edit collections
Transforms data flow with adaptable schema and smooth public cloud deployment
What is our primary use case?
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.
What is most valuable?
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.
What needs improvement?
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.
For how long have I used the solution?
I have about four years of experience working with MongoDB.
What was my experience with deployment of the solution?
The deployment process was straightforward.
What do I think about the stability of the solution?
MongoDB is highly stable, and I would rate its stability at nine out of ten.
What do I think about the scalability of the solution?
MongoDB is highly scalable. I would rate its scalability nine out of ten.
How are customer service and support?
We use the open-source version of MongoDB and manage it ourselves, so we have not contacted their technical support.
How would you rate customer service and support?
Which solution did I use previously and why did I switch?
Before using MongoDB, we used IBM
DB2. We switched to MongoDB to develop a composite system that includes both SQL and NoSQL databases.
How was the initial setup?
The initial setup of MongoDB was a straightforward process.
What's my experience with pricing, setup cost, and licensing?
We use the free version of MongoDB, so there are no licensing costs.
What other advice do I have?
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.
Which deployment model are you using for this solution?
Public Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Other
Overpriced, Poor performance and some of the worse support I have ever had to deal with
What do you like best about the product?
I have nothing good to say about MongoDB Atlas.
What do you dislike about the product?
My story with Mongo began when I started a new software position, and they had a legacy version of their software product using Atlas.
Compared to our other infrastructure bills, Mongo was significantly higher for the amount of compute and storage we used ($3K per month). This is a managed service, so you would expect to pay a premium. Ok, sure, but then I expect great functionality, performance, and support.
The main problem began with Mongo when we needed to delete some data because they tie the CPU and memory tiers to storage size, so we were overpaying. Our application would run fine off an M10 dedicated cluster (the smallest tier), but it had automatically scaled to an M50 because of storage. This is already a bit disappointing because they are forcing customers to pay for more compute and memory than they need.
So we started deleting some data, but then we ran into problems. The data deletion process was really slow and also slowed our entire cluster down, causing lag and performance issues for our end users. But hang on, this makes no sense because we are paying for more CPU and RAM than we need, so why would we have this issue?
It took us three months to delete 500GB of data. In the meantime, our bill remained the same because you can't claim the space back without compacting the database. Ok, fine. So we ran compact(), but we only freed ~100GB on the secondary clusters.
Support gave us a script to run that can see how much storage can be freed.
In the end, we had to activate an expensive additional support plan costing us $500 USD per month to get support to run a re-sync command. This should have taken their support people 10 minutes, but instead, they mucked us around going back and forth on the ticket, taking three weeks to resolve.
A year later, we needed to delete some more data. We spent another five months deleting 800GB of data. Then we ran compact() and freed 300GB. Where is our other 500GB? We contacted some humans at Mongo, who really couldn't do much other than suggest we get funding to cover the $500 support for one month. Yes, we got the $500 credit, but when I went to reactivate support, it was going to charge us for three months for one month because Mongo retroactively bills you for three months when you reactivate. Wow, we started in a bad place, now I'm beyond frustrated; this is daylight robbery.
To this day, I am still fighting to reclaim some storage, but at this point, I'm going to recommend to our CEO that our dev team put some effort into moving away completely from Mongo.
I also need to mention that Mongo recommended we use their online archive features, but when we crunched the numbers, it was still quite expensive, and we would have to do significant work to make our application work between the regular clusters and online archive. So it was significantly more logical to just put the data in AWS S3, then delete it in Mongo.
If I can summarize my experience with Mongo, and I acknowledge mine is probably quite different to most, here it is:
Overpriced for the performance you get
Sneaky billing model where they tie CPU and memory to storage
Terrible and expensive support
Sneaky extra charges on reactivating support
Bad support escalation solutions - they couldn't just turn on free 'support'
Poor database performance
Slow delete operations
Ecosystem lock-in
Forced upgrades - no LTS releases
Let me sum it up this way: if your compact() command does not free up the space that is available on your cluster, then provide the customer with free support to do so.
I hate dealing with Mongo. Nothing is simple, everything is expensive, and the performance sucks.
If you are considering using Mongo, find something else. Even if you have to take a bit more time to learn AWS Dynamo, S3, or Aurora, you should do it; you will save time and money in the long run.
Mongo, you deserve this negative review. I have given you plenty of opportunities to resolve things and have escalated issues, but you just don't care.
We wanted to move away from Mongo before; now I can't get rid of it fast enough.
What problems is the product solving and how is that benefiting you?
A simple managed database to get up and moving quickly as a developer.
