MongoDB 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 use it as a data warehouse, and we also use it for software development when we are not sure how much and what kind of data we would get from the customers. For a short span of time, we also use it for the data lake to dump the data of a temporary instance so that the developers on the next node can leverage this data.

I like the schemaless architecture that it follows. I also like the sharding that it provides.

Its security features can be better. Sometimes, my higher authority says that we are not going to use MongoDB because it doesn't provide that much security for the RDBMS or relational data that we use for transactions. Instead of MongoDB, we will use Oracle Database because for a transactional service, you have to rely on RDBMS ACID properties.

I would love to work on MongoDB by using my mobile phone. When I am working remotely or traveling and have some instances deployed on my server, I should be able to check through my mobile whether all the data is being pulled. GitHub has a similar feature, where it lets you read from the laptop, and you can also pull and push with your mobile phone. I would request MongoDB to provide such a feature. Basically, I want a mobile version for both iOS and Android versions.

I have been using this solution for two years.

It is stable.

Currently, we have about 1,000 to 5,000 employees all over India. Most of us are using MongoDB for internal projects. 

When I was getting trained in the data engineering field, there was a saying that if we run after the technical support, we won't be able to see a bug in our own code. Since then, our superiors or colleagues don't suggest going for technical support. If anything goes wrong, we just troubleshoot it on our own, and we have done that successfully.

When we provide solutions for a customer, we look at the domain in which we are working, and accordingly, we recommend or select a database. It is up to the customers which database they want to use and how they are going to use it and leverage the subscription of that database. Do they want Oracle, MySQL, or SQL Server? Based on their preferences, we select the database.

For people or businesses that are currently trying to put their feet in the industry, it is a good thing to start their career with MongoDB. Sometimes, you don't have the knowledge of SQL and how to put a query to get a result. In MongoDB, there are certain things that make it different from other solutions. It is schemaless, and you don't have to have the knowledge of schemas. It is a good way to go ahead.

I would rate MongoDB a nine out of ten.

I use MongoDB mainly on the back end. We use it in my company for the database, and I also use it on my side projects.

MongoDB is a NoSQL tool that gives us much freedom to manipulate how the data works.

MongoDB should support TypeScript.

I have been using MongoDB for about two years.

I haven't had any issues with the solution's stability. However, our company faced an issue where the tool was down for two hours.

I rate the solution’s stability an eight out of ten.

MongoDB is a very scalable solution. About 10,000 users are using the solution in our organization.

I would recommend the solution to other users because it's scalable and easy to learn. The solution's documentation can be rigid and hard to understand initially. Users should use an ORM or ODM. They should use Prisma or Mongoose as the starting point for using MongoDB. MongoDB is easier to manipulate. The easier the onboarding, the harder the maintenance.

Overall, I rate the solution an eight 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.

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.

 MongoDB has been installed in our active presentation in Google.

The most valuable features of MongoDB are the variety of translations available and the ability to deploy it on the cloud is useful. The cloud users can access the data, work on the data, and if they want to import or manipulate some data they can.

MongoDB can improve large-size video or media frame operations. There are a lot of customers who want to upload media frames and video games but there is some difficulty. In MongoDB, we are looking out for solutions that are for large-size media files that can be saved and navigated efficiently.

I have been using MongoDB for approximately one year.

MongoDB is stable. It has been more stable in the cloud than what we experienced when we used it on-premise.

We are using MongoDB in the cloud and it is scalable.

We have approximately two clients using this solution.

The support from MongoDB has been great. We had to work with them during the data validation and support was helpful. There were a lot of people giving revisions during the migration and they were working for every dollar paid. We had a good experience with their technical support team.

The initial setup of MongoDB is a little complex. The setup was not complex but migrating the data from the existing resources to MongoDB was tough because we had to do some migration. If there was a better migration tool it would help.

I would recommend this solution to others. Having the solution in the cloud was a very clean process.

I rate MongoDB a nine out of ten.

Our primary use case has been for maintaining video content and varying it. We are an enterprise-level organization with around 500,000 employees internationally. The company has over 10,000 users of this solution. I'm an integration solution architect. 

The solution is user-friendly with a good object retrieval feature. There are no joins, queries are fast and the product provides helpful drivers. I like the abstraction layers. 

I'd like to see improved scalability and elasticity. Also, the software should have certified container images so it can readily be used in production.

I've been using this solution for two years. 

The solution is stable. 

More could be done to improve the scalability.

Customer support is at a reasonable level. 

The initial setup was pretty simple. It's a good product for academics since it's an open-source solution so it's readily accessible with fast onboarding. Deployment was carried out in-house. There is no maintenance required.

I rate this solution seven out of 10.