MongoDB Enterprise Advanced Reviews

I used the solution in the production level to search data and create education-based tutorials for a project. We had 30 managers, senior architects, tech leads, and software engineers working on the project. 

Currently, I use the solution for my personal work. 

The solution has good flexibility and very fast performance for searching data in comparison to Oracle DB or MS SQL.  

The solution is very lightweight and uses JSON syntax to insert or search records. 

The solution should more easily integrate with custom code. 

Kibana should be bundled with the solution as one software installation. Currently, Kibana is a separate software and that is redundant. Most developers install both Kibana and the solution so a bundle would make the process easier. 

I have been using the solution for seven years. 

The solution is good and stable for my use cases so I rate stability a six out of ten. 

The solution is scalable so I rate scalability a nine out of ten. 

I have not used technical support because I can easily Google to find the information I need. 

My company also uses Oracle DB and MS SQL. 

The setup is one click and very simple. Installation only takes a couple of minutes. I rate setup a nine out of ten. 

I installed the solution on my personal laptop. 

I currently use the solution's community edition which is free. 

My company considered performance against Oracle DB or MS SQL and decided to use the solution for a particular project because its performance is very fast in comparison. 

Neither the solution nor Oracle DB is suitable all the time, so selection depends on use cases. The solution is definitely the top choice when there is no SQL database requirement. 

I really like the solution and rate it a nine out of ten. 

We are using MongoDB as the database on our private cloud, which is AWS in Frankfurt. We monitor different banks adopting cloud in our infrastructure with confirmation and approval by the local regulator.

It's mostly all related to our digital part of the bank. We are using MongoDB for the process of onboarding new products. When we are actually calculating proposals for mortgages or complex financial products, we must consider that calculation across many sources. Some are internal. Some are external. We are using MongoDB to support the calculation, or proposal calculation, on our web digital processes.

It has given us a chance to explain to people that once the bank made the decision to migrate some processes, applications, and data into the cloud, it was a benefit. It's not only about the change of process and application components. It's also about changing the data ownership and data operation. It went hand to hand, actually, to start training them that there is something beyond Oracle and SQL databases. With Mongo, our team was happy to get a chance to learn something new.

However, once some people learned the solution, we did lose them to someone else. It shows how popular it is not only in our organization but in other places as well. 

We are actually taking MongoDB as serious database tech. It has visible benefits, actually, in terms of price of ownership if you compare it to, for example, Oracle. When we have some processes which really don't need Oracle, we use MongoDB as the first option to see if it wouldn't be cheaper to leverage.

Oracle is stronger in some specific features. For example, the remote cluster when you need to synchronize the enormous amount of data between two data centers and so on. It's that Oracle RDC and Oracle GoldenGate which are actually about the data replication which can be supported by the nature of the database scheme.

We are actually validating each process and also new processes in what we call the operational tier, and if you are having the business-critical processes which need business-critical operative data, we actually prefer Oracle over MongoDB.

MongoDB is the second option for non-critical processes at the moment. We do not yet trust it with critical items.

We'd like information about client onboarding experience and success stories. It would help to have something to show to internal stakeholders. We need some use cases from the outside to begin to open discussions on how we can use this tech in more ways in the future. 

It started during the HR transformation. I would say we started using it four years ago.

For the scenarios we have chosen, it's ranked as stable. We also made what we call disaster recovery scenarios to be able to replicate MongoDB content after failures. So far, it's been very good. It's fast.

We can scale. It went hand in hand with our AWS journey and the cloud infrastructure. I remember one scenario which involved a quite popular product in the Czech Republic. It was for savings accounts. We were actually able to predict the number of interactions being required during the phase of the product opening. We prepared the infrastructure from MongoDB and after that period of time, it was shrunken back to the original size. It scaled up and down exactly as we needed it to.

We have seven internal people including analysts and data engineers working on the solution. They are creating tables and preparing the data loads.

We use it quite a bit now. We take it as the second option for initial test requirements in regards to the data infrastructure. The Oracle Database is not the first choice anymore, except for the Tier 1 pieces of processes.

I am not involved in the troubleshooting. I don't directly deal with technical support.

We also use Oracle. Previously, a long time ago, I also used Teradata.

In Asian companies, Oracle is very expensive to use and adopt.

We started using Mongo as it was stable and easy to adopt.

The first time we deployed the solution was actually a limited implementation to support the proof of concept. When we had some objectives when it came to validating how fast we were able to prepare Mongo at AWS, it was quite simple. 

The second part was actually to handle the initial training of people for operation and administration. Now we actually have some results which prove that the MongoDB way is a positive way and this is what we ended up using for other scenarios.

I would say that if you would were looking for 100% of the data in the cloud, MongoDB would currently be at 50%.

The initial proof of concept was scheduled for three months.

We did the cloud adoption in-house. We did have help with a few complex elements. It was supervised and everything was finished by the internal code team to improve their skills and learn what was needed to have BAU, business as usual.

The ROI is fair. I'd rate it 2.5 out of five in terms of getting a return on investment. It's something, on our side, that we still have to fully adopt, however. 

I was trying to compare pricing in terms of how much it would cost to do a TCO. We are counting TCO five years ahead - the total sum of money. It was 2.5 times cheaper than Oracle. I would say it's stable and the pricing is reasonable.

This is the first time we've adopted the cloud and therefore it's hard to truly gauge how much it will be a year out. It could change. It's difficult to accurately predict.

We faced some extra costs for what we call the L2 and L3 additional support.

We are customers and end-users.

We are likely on the latest version right now. 

You can adopt this tech very easily on your private cloud. AWS is actually broadly supporting and it's not only us but also a lot of other industries. MongoDB is not unknown. There are a lot of people that have experience with it, however, it's very important if you have a company that is evaluating something, to have the ability to execute. 

If you have some local vendors, if you have international vendors, if you have some integrators that are able to use it and to help you use it, MongoDB is a good choice.

I'd rate the solution five 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.

Positive

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 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.

The primary use case is data retrieval. It allows for easy retrieval of data as all the required information is stored within the document. This becomes particularly useful as the company scales, preventing queries from becoming sluggish.

Working with it extends beyond database skills. Utilizing additional tools such as ML frameworks (e.g., TensorFlow), languages like Python for data analysis, and platforms like Apache Spark for distributed computing can enhance one's capabilities in extracting meaningful insights from data.

It facilitates the generation of heatmaps for graphical data analysis. This can be valuable for visualizing patterns and trends in data. While other databases like Cassandra may also serve this purpose, MongoDB stands out for its simplicity in handling complex queries and graphical data representation.

It has certain limitations when it comes to handling hierarchical data, enforcing relationships, and performing complex joins, which should be taken into account when designing databases for applications with intricate data requirements.

I have been working with it for a year now.

It is highly stable. I would rate it nine out of ten.

It emerges as a favorable choice for customers seeking efficient data storage and scalability. On a scale of one to ten, I would rate it at eight.

I have been working with both MongoDB and HIVE and the choice between them depends on the specific requirements of the client. While I've been actively engaged with both databases, the preference depends on the nature of the data and whether file storage is required. If data retrieval is the primary focus without the need for file storage, I opt for MongoDB. On the other hand, if the client requires storage for both data and files, HIVE becomes the main choice.

I would rate the initial setup six out of ten.

I only used the open-source version.

Opting for MongoDB could be beneficial, especially for storing large volumes of records, even for transient data. The decision hinges on the nature of the data itself. If there is a significant amount of metadata, it becomes a preferable choice for its scalability and superior query performance. It's important to anticipate future operations; for example, if there's a current load of ten thousand audio and video files, MongoDB can efficiently handle it. Overall, I wold rate it eight out of ten.

Our company uses the solution for small functions that contribute to some projects. The solution is part of our whole company architecture. Some projects might not touch the solution but the data is always there when needed. 

The reports are useful for controlling the load of our whole architecture. We compare different reports to different levels, applications, and modules of our whole architecture. 

The solution could include more integrations with other platforms. 

One customer is a hospital that uses a specific program with certain authorizations. The solution may not be able to integrate because the authorizations are different. 

I have been using the solution for eight months. 

The solution is stable. 

The solution is scalable. 

We previously used another database but it was not proper for our tasks. The solution provides us with the proper resolution and results. 

The setup is very easy. 

I recommend the solution if you intend to tie it to your business architecture. I rate the solution a six 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.

MongoDB is easy to use. 

It isn't easy to recognize entities with MongoDB. 

The tool's installation is easy for anyone with experience in databases. 

MongoDB's pricing is reasonable. 

I rate the solution a six out of ten. 

I work with multiple personal applications, and for that, I use MongoDB and SQL Servers. Depending on the use cases, I choose MongoDB, as it is not a heavy application. Usually, I use MongoDB for attachment sections because RDBMS is heavy for attachment software. I also use it for assessments. Sometimes, I store data for a time-series database, such as stock market data, which I analyze using MongoDB.

Feature-wise, I like how MongoDB stores attachments because it allows me to store the results of the attachment and pull them up whenever needed instead of having to generate them every time. I can save those results as PDFs and other formats rather than just saving the data and then having to regenerate it. This approach enables me to analyze the attachments and research existing data, making it easier to retrieve information when needed. Overall, MongoDB has helped manage and analyze attachment data.

I cannot comment on how to improve the database since I am not an expert in that field. It is important to note that MongoDB has limitations since it can only be used for specific use cases. For example, for master data, I would want to pick keys using an RDBMS, but for attachments, I would choose MongoDB. Other than that, I am more familiar with RDBMS databases.

I have experience with MongoDB for three to four years and am an end-user of the solution.

It is a stable solution. Stability-wise, I rate the solution a nine out of ten.

It is a highly scalable solution. In fact, a friend of mine who works as a stock market analyst and operates using one of the popular websites in India also uses MongoDB for his work and finds it very efficient. Scalability-wise, I would rate the solution a nine out of ten. From my end, only four to five people use the solution, but from my organization's perspective, around 500 users are utilizing it.

We used to handle technical support ourselves, as the tool was easy to handle, and we didn't need any special assistance. Although we never had to interact with technical support, I would rate it a nine out of ten.

Positive

Previously, I used RDBMS but found it a bit slower. That's why I switched to MongoDB for analytics purposes. I had also tried using MySQL long back before using ClickHouse, but after that, I didn't use MySQL again. While using MySQL earlier, I faced some performance issues while writing a lot of entries. So I shifted to MongoDB, which has been working well for me. Although MySQL is an open-source solution, its performance was lagging. I also tried using Oracle, but it was a costlier option.

The initial setup of MongoDB was easy for me, and I found the community support to be very helpful. I rate the initial setup process a nine out of ten. The deployment process was also quick and only took a day or less. All that was required was to install the solution, which didn't take much time. I deployed the solution on my own, and it doesn't require any maintenance. As a friend and I only use it, it is for personal use only.

I chose MongoDB because it is cost-effective compared to Oracle, which can be expensive. In addition, MongoDB has good performance and has not caused any issues while working with it. It has been a good choice for me.

I recommend MongoDB because I haven't experienced any issues with it so far. Therefore, I would definitely recommend it to others. I wouldn't give the tool a ten out of ten since there is always room for improvement. I rate the overall solution a nine out of ten.

We use MongoDB to build online applications.

The most valuable feature is the speed of MongoDB.

The scalability of the solution has room for improvement.

I have been using the solution for two months.

The solution is stable.

The solution is not really scalable. I give the scalability a six out of ten.

We have ten people using the solution and we plan on increasing the number of users.

The initial setup is straightforward. The deployment time was within one week.

The implementation was completed in-house.

I give the solution a seven out of ten.

Three people are required for solution maintenance.

We chose MongoDB because of the speed.

MongoDB is a good solution and I recommend it to others.