MongoDB Reviews

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.

It stores historical data with ease. For example, if you are a healthcare member, then you will have multiple records of visits to the doctors. To store such data in Oracle Database, you have to create many records. You might also have duplication problems because your records are going in again and again, because of which the data warehouse and the maintenance cost will be huge. MongoDB is comparatively lightweight. It is a JSON extract. Once you define a schema and extract it, you can push all the relationships in any way you want. It is easier to define and get different types of transactions into MongoDB. It is also easier to set it up as compared to other solutions.

MongoDB is a NoSQL database, which means it is a document DB in which you can store documents that you created in BSON. It is pretty fast in response. It is faster than relational databases because it does not define any primary keys, secondary keys, tertiary keys, and all those kinds of things.

A normal Oracle or database tester will take some time to gear up to MongoDB because the way of writing queries is different in MongoDB. There should be some kind of midway where a person who is coming from an Oracle background can write a query and get a response by using something like a select * statement or other such things. There should be some way for MongoDB to interpret these commands rather than making a person learn MongoDB commands and writing them. I struggled while writing these MongoDB commands. I had not seen such queries before. It was pretty difficult to get them. This is one of the areas where it would help from the improvement standpoint.

It is stable.

It is scalable.

I haven't called them ever. I haven't seen any issues, so I would say it's pretty good.

I don't know, but I have heard from people who procure it that it is much cheaper than Oracle.

It is a good tool. You should give it a try. It has a very good and different perspective of looking at how data can be stored in a database and restored from a database because it is not like a straight query or record. The data is just in files. It does whatever a database should do.

I would rate MongoDB a nine 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.

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.

Our main purpose is to build a historical data lake containing commercial data of service usage, products, retailers and wholesalers, AAA sessions, account recharges and transferences, et cetera. We're looking at the whole company's key internet usage data from a commercial perspective. 

The data lake holds data from 5 years, so now we're triggering ML processes. The impact of the product in the community has been huge, and by that I mean, the community of developers that make apps for the telecommunications market. 

In our country, we were pioneers at using MongoDB. Now, at least 7 developer teams are using it.

It fits well. Data we want to keep represents facts. We've ETL'd from operational systems, so there was no need for relational engines. MongoDB allows us, through replication and sharing, to build a robust platform for keeping data. Even when volume starts to be an issue it is really easy to escalate horizontally and connectors do the rest. 

I thought that choosing javascript as the internal query language might be a mistake, but that was a very good choice. 

The fact is all of our users are happy with the stability and quick response times our solutions have. All of them are based on MongoDB as a persistence engine.

MongoDB is very stable, and, by that I mean, amazingly stable. Using javascript was the key to have a wide range of functionalities based on its syntax and grammar. The aggregation framework is really good, allowing a developer to build very complex queries. The fact a developer builds some functions and can run through the whole cluster, actually and efficiently using all the resources available, is a great advantage. It's given the developers a very robust path to building whatever procedures they need. MongoDB is very, very stable.

The product roadmap shows us that the MongoDB team is very professional. Since we started using the product, it's like having wizards that are one step ahead of our needs. They are looking at the community, the market, and the competition. 

From my point of view, they need a totally free IDE to work at high levels. The best I know is Studio3T, but licensing and an embargo in our country make it difficult to access. MongoDB needs something like Studio3T (Compass is just fine, but it isn't Studio3T) to really get the whole developer community.

I've used the solution for more than six years.

We are using this solution for storage and mapping. We have developed an application for mapping. We source the GeoJSON position for approximately eleven million points.

Our application is made for desktops, and we started using MongoDB for that application.

We have four teams in our laboratory and the other teams are developing their applications to be used with MongoDB.

The primary use of MongoDB is for mapping the application.

The most valuable feature is the geometric information done with GeoJSON. We search the points, and we can perform searches for the data using the RPA for MongoDB. We can ask for points in the geometric shapes or points near other points.

Simplifying the aggregation framework would be an improvement.

Also, the replica system could be improved.

I have been using MongoDB for approximately four years.

We have anywhere from 15 to 20 people using this solution on a daily basis.

This is a stable solution and we are happy with it.

It is possible to scale this solution but it has a problem with the replication.

I have not contacted technical support. We have been able to resolve any issues we have had on our own.

Previously we used MariaDB and we continue to use MariaDB for other purposes. We started using MongoDB to manage non-relational data.

The initial setup was simple and straightforward.

It took a couple of hours.

I am the only one maintaining this solution.

I completed the initial setup and implementation.

I did not use the help of a vendor or integration. I did it on my own.

MongoDB is an open-source solution.

We are a small laboratory and always look for open source solutions or solutions where the licensing is every year. We are prepared to do the troubleshooting ourselves.

The most important part is to evaluate the rest of the ecosystem integrations. When we started, the driver that we used to develop the application was in C++ wasn't relevant. At the start of the project, we had some difficulties because there was no documentation available. There were no examples and no support from the community. 

You have to take into account the languages that you are using. If you are going to develop in the main languages of the solution such as Python, or JavaScript, then you won't have to worry.

I would rate this 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.