MongoDB Reviews

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.

Sharding is an excellent feature of MongoDB. Atlas is an awesome feature of MongoDB that makes life easy.

People coming from RDBMS should have the flexibility to write queries in SQL that can be converted into JSON queries. This feature is working, but we can still achieve some integration and make it more flexible. It is not as easy as writing direct queries on Atlas. This feature will definitely increase a lot of users. RDBMS users think this is a different query language, MQL, which is uncomfortable for them.

I have been using MongoDB version 6.0 for more than 12 years as a customer.

MongoDB is a very stable solution. The solution is a leader, according to the Gartner report.

In our company, around 2000 to 3000 people are using the solution.

MongoDB’s technical support is awesome.

Positive

We previously used the RDBMS solution, MySQL.

MongoDB has five to ten times better performance than MySQL. MongoDB has a lot of advantages. With MongoDB, you can store any kind of data, including structured, unstructured, and semi-structured data. MongoDB has a lot of benefits over RDBMS.

It is very easy to deploy MongoDB. If it is deployed on-premises, it takes an hour. It does not need any additional prerequisites or configuration details.

We have seen a lot of ROI with MongoDB because it gives five to ten times better performance.

MongoDB's pricing is not reasonable, but it is not as expensive as the others.

The solution's pricing depends on the deal, which includes how long you will use it and the number of deployments. They do not have a fixed cost. However, the maintenance and support cost is included.

I am satisfied with the product.

Our organization has a DBA team of 50 people. However, the work for the DBAs is minimal because MongoDB intends to have zero DBA. They want to develop a product where the need for DBAs is minimized.

Users should approach MongoDB with an open mind, without thinking that it's a different technology altogether and has a different language. Users don't need to develop the application based on a schema. They can develop the application, and the schema will follow.

Overall, I rate MongoDB an eight 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. 

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.

We installed MongoDB on an EC2 instance and used it. 

Use used MongoDB for a NoSQL use case.

The most valuable feature of MongoDB is the ease of connections, aggregation, and queries. Additionally, there is plenty of documentation available for assistance if you require it.

MongoDB should incorporate more features, particularly search functionality, and real-time communication capabilities, to improve the database and provide data listening services. Currently, we rely on the Atlas offering, but it would be fantastic if MongoDB could develop a new solution or updated version that includes these features within its internal database and driver. However, I am uncertain if this would be a viable or profitable move for them, and I am speaking from a mobile-centric viewpoint.

I have been using MongoDB for approximately six months.

The solution is stable. However, I recall instances when the database crashed due to high-volume querying, but this can occur with any database if the queries being run are not optimized for the particular instance.

I rate the stability of MongoDB an eight out of ten.

I rate the scalability of MongoDB a seven out of ten.

We were using PostgreSQL for everything, but it is not the best fit for our needs due to the diverse nature of our data. We switched to MongoDB, as NoSQL is better suited for this scenario.

I have received a return on investment using MongoDB.

The pricing is favorable if you opt to install MongoDB on an Amazon EC2 instance as you won't have to pay for the extra Atlas services and can instead manage the scaling yourself. This allows for a cost-effective solution and using MongoDB on a small scale, I have been able to utilize it for free.

I rate the price of MongoDB an eight out of ten.

I rate MongoDB an eight out of ten.

We are using MongoDB for storing user information and our customer data. If an application requires to save information, that data is stored in MongoDB.

MongoDB has helped my organization by being able to handle large amounts of data. Nowadays, if users are using our application all the data we store in our database. If you're trying to receive the information from the database, it's important we are able to retrieve the result as quickly as possible.

The most valuable feature of MongoDB is the NoSQL database. In a SQL database, we need to join data together with a unique ID amongst other things, but in MongoDB, it's not required. We can directly receive all the information. The performance is very good. Additionally, they have frequent updates.

I have been using MongoDB for approximately two years.

The stability of MongoDB is good.

MongoDB is scalable.

We have approximately seven backend developers using this solution.

The MongoDB community support is very good. I was facing some problems using some queries, so I posted my issue on that community channel, and within one day, I received a solution. The support was very quick.

I rate the support from MongoDB a four out of five.

I previously used MySQL and PostgreSQL, but I had permission and licensing issues with MySQL. I prefer MongoDB over others.

The initial setup of MongoDB is straightforward. We only had to use a few commands to install it.

There are different licenses available to be purchased, such as individual, premium, or enterprise.

I did evaluate other options before choosing MongoDB.

Only one person is required for the maintenance of the solution.

I recommend MongoDB to others.

I rate MongoDB 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.