MongoDB Enterprise Advanced Reviews

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.

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.

I use MongoDB in our company since our overall data is stored on it. My company uses the tool as a back-end database.

The most valuable features of the solution are that it appeases our company's needs and is quick.

The dashboard is an area of concern in the solution where improvements are required. The price of the product may be a concern for the customers who use it, so improvements may be required in the pricing model.

I have been using MongoDB for more than ten years. I use the solution's latest version.

Stability-wise, I rate the solution an eight out of ten.

Scalability-wise, I rate the solution a seven out of ten.

It is always important to ensure that users use the latest version of MongoDB if they want to use the scalability features of the product.

There are more than 1,000 users of the solution.

My company plans to expand the number of users of the product.

MongoDB's technical support team is responsive and helpful.

I rate the technical support an eight out of ten.

Positive

The product's initial setup phase was quite easy, but for some use cases, it was a bit difficult.

The solution is deployed on the private cloud services offered by AWS.

The solution can be deployed in a couple of hours. The product's initial deployment phase can be taken care of in an hour, and if any further deployments are required after that, it takes less than an hour.

Six to seven engineers are required to take care of the deployment and maintenance phases of the solution.

The integration capabilities of MongoDB are fine for the solutions that we use in our company.

The tool's vendor takes care of the product's maintenance.

I rate the overall solution an eight out of ten.

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.

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.