I used MongoDB Atlas for structured data storage as part of an application service provided to one of our customers. The application was based on MongoDB and Atlas. While Google Cloud SQL was used for consulting, I interacted with Google Cloud but was not the final decision maker.
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Supportive features enable effective data management and growth
What is our primary use case?
How has it helped my organization?
From an operational point of view, there were no costs associated with maintaining the database on my side, and service costs were acceptable from both my side and the customer’s perspective.
What is most valuable?
I find MongoDB Atlas highly scalable and easy to use, with very good support. The pricing is quite scalable and applies to various scenarios, both for smaller and bigger companies.
MongoDB Atlas has supported our data growth well, and my overall impression is very positive. It is easy to work with and has a reliable support structure. For structured data storage and performance, it provides a comprehensive solution, and the feedback was generally positive.
What needs improvement?
I am not an expert on what improvements could be made to MongoDB. The service is continually evolving with new features while maintaining reasonable pricing, making it attractive for developers.
For how long have I used the solution?
I have been using MongoDB Atlas since 2017 and Google Cloud Platform since 2018.
What do I think about the stability of the solution?
There are no issues mentioned regarding stability. I evaluated MongoDB Atlas as not the best solution for the application in the long term, specifically when the services consolidate themselves.
What do I think about the scalability of the solution?
MongoDB Atlas scales well and supports data growth effectively.
How are customer service and support?
The technical support is very good. I have used them sometimes, even recently, and found the feedback to be spot on our needs.
How would you rate customer service and support?
Neutral
What's my experience with pricing, setup cost, and licensing?
The pricing is quite acceptable and scalable. For our service, it was around 300 to 600 euros per month, which was acceptable for our customers. We could scale up for better performance and scale down when needed.
What other advice do I have?
I highly recommend MongoDB Atlas for both smaller and larger companies.
It is rated an eight out of ten, depending on the use case. As a document-based database, it is one of the better products on the market.
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Other
Audio embedding resources
I’d like to suggest adding more resources on using audio embeddings with MongoDB's vector search. Additional guidance on best practices and examples would greatly benefit those looking to work with audio data in MongoDB.
Powerful and Flexible Database for Gen AI Projects, with Room for Onboarding Improvements
Creating Mentation, an AI-driven wellness assistant, was an enriching experience, and MongoDB supplied the foundation we required for effortlessly handling intricate and diverse data. By managing user interactions and emotional data as well as processing vector embeddings, MongoDB effortlessly fulfilled our requirements. Its adaptability and scalability proved essential, allowing us to broaden our project’s scope without having to repeatedly reconfigure the database.
Although the documentation is comprehensive and addresses various use cases, a concentrated, beginner-friendly crash course would have been immensely helpful—particularly for teams such as ours seeking to utilize AWS and Gen AI. Exploring the fundamentals of MongoDB, such as querying, vector indexing, and aggregation pipelines, prompted us to seek out external tutorials, especially to clarify information regarding vector indexing. At one stage, we came across contradictory data from these sources indicating that solely larger M10 clusters were capable of handling vector indexing, which resulted in additional testing and problem-solving.
Although there were some learning challenges, MongoDB demonstrated to be a robust solution for the requirements of our project. By providing a more efficient onboarding process—centered on key elements and better instructions for utilizing features such as vector indexing—MongoDB would become even more attainable for developers engaged with advanced technology. In general, we had a positive experience with MongoDB, and with some modifications, it could easily become the preferred choice for any developer venturing into Gen AI applications.
Improvement on Documentation
For my hackathon project, I chose MongoDB Atlas from AWS Marketplace. I particularly like the auto-scaling capability.
However, I encountered some challenges with the SDKs at multiple stages of use, so I had to look outside the official documentation for help. For example, while connecting to the cluster.
While the existing documentation is okay, it would be more beneficial if video resources were included (as this helps better than textual documentation). Additionally, integrating real-world examples and case studies into the documentation could greatly enhance its practical value.
The best solution out there
I've used mongodb professionally for 4 years and have found the product meets and exceeds the demands placed on it by the products i create.
MongoDB
What do you like best about the product?
One thing is that its a NOSQL database, meaning no schema required. Also, main thing i liked in mongodb is simplicity. We can define our data through objects (like document format or the json format). Interface of mongodb is calm and greenish making it very easier to create, edit and deliver the data. Also, there is this thing called "sharding" which i learnt in my studies, what happens is the high amount of data can be shared across multiple servers. This was the coolest thing i got to learn.
What do you dislike about the product?
Well. dislikes about mongodb, is linked to the disadvantages of nosql itself. Not particularly the mongodb, as the nosql doesnot require any schema to be defined. so in my personal experience, i used to get frustated when i noticed many object data got messed up. As i switched from sql. the table format habit was printed in my mind for months, took some while to understand the hierarchy of data being created in the nosql format or rather json format in mongodb (might say complex nodes affect the data).
What problems is the product solving and how is that benefiting you?
So i was working on a project where we needed to show updates on the app as soon as they happened. It was important because our app had to stay current with new info all the time. So we picked mongodb as our database. Also when more users had to use the app, we could add more mongodb servers easily without any crashing or slowing the app down. Obviously its a database, it helped to store large amounts of data organized in this json format. If you are working on a real time project that needs to show updates quicker, mongodb is a great choice!
Future of Databases: A Critical Review of MongoDB
What do you like best about the product?
What I like best about MongoDB is its flexibility. Most of the database require fixed schema bt MongoDb allows you to store data in a more dynamic way and document based format. This document based structure is very straightforward and very easy to use even for the newcomers. It is very flexible.
Also setting up MongoDB is quick and it integrates very easily with other platforms.
MongoDB offers strong customer support, with plenty of documentation, community forums, and professional support options. Because of all these features it is frequently and widely use across the globe and industries.
Also setting up MongoDB is quick and it integrates very easily with other platforms.
MongoDB offers strong customer support, with plenty of documentation, community forums, and professional support options. Because of all these features it is frequently and widely use across the globe and industries.
What do you dislike about the product?
However MongoDb is less efficient for complex queries and transactions than traditional databases. Also while handling lare datasets it require very large memory and storage which can be costly.
Proper indexing in MongoDb is also very crucial. Also sometimes it leads to data redundancy.
Proper indexing in MongoDb is also very crucial. Also sometimes it leads to data redundancy.
What problems is the product solving and how is that benefiting you?
MongoDB solves several problems related to data management. Earlier traditional databases required a fixed schema which made difficult to handle large data. MongoDb allows flexible schema which allows us to easily change data models without much downtime.
Also there was problem with scaling and it was very costly. MongoDb solved this problem also. It can be scaled horizontally by adding more servers in les cost.
It handles large volumes of data without degrading in performance.
Also there was problem with scaling and it was very costly. MongoDb solved this problem also. It can be scaled horizontally by adding more servers in les cost.
It handles large volumes of data without degrading in performance.
Offers other benefits like high performance, document-oriented storage, and flexibility in data modeling
What is our primary use case?
We use an application called Fully Factory in the Indian stock market. It works by setting price ranges for stocks. For example, if the Bank of India stock sells between 800 and 810 rupees, you set a range of 800 to 850 rupees. The system prioritizes buyers with the lowest price and highest quantity.
MongoDB's "find first" function quickly locates and blocks the remote and quantity. The client's amount is shown in the record, and then it's processed. We take around 500 records, and the first 100 are processed in a batch. This gets executed and recorded. Developers handle tasks like JP, AR, and AP separately. We update the client's inventory and pass it to a third party. In Microsoft, we use the same client cover to determine the quantity and product details. This is then executed in their API Acondra server system.
So, MongoDB Atlas is used in stock market applications to handle large-scale data processing.
What is most valuable?
For security reasons, I prefer MongoDB Atlas. It supports role-based access control, so you have an entity for each individual.
Spring Cloud ensures I have this set with Atlas, and Spring Security is entering the security for me. That's why I feel Spring Security is much better. Even if you expose a public method, it will be exposed via an authentication token.
If you're putting a direct authentication case authentication with their sync of Google token, just put a sync token directly. It will automatically type your method. Even if you expose a public method, it will be exposed via authentication token. Unmasked analytics, you have PeerSpot on or authentication token. It won't get executed.
What needs improvement?
It's better to use the predicate in Java side to sort. If you are trying to sort in MongoDB, the comparator of Sandal will be discussed. It can be sorted, but if you can do the competitor in Java, sorting using predicates (filtering conditions) and all, it'll be faster. That is what I noticed. For conditioning sorting in MongoDB might be slower, but I haven't verified that. I am doing sorting using predicate in Java.
Another concern:
When I use RoboMongo with MongoDB, it gets delayed and slower when the records are more than one billion. If the records are more than one billion, the document page will see it's all documents. If you have more than a thousand series in your system, it will be difficult to scroll down and get the reserve the directory. I think if they can have some horizontal way of displaying the reports, they can't be answered, but I'm not sure. The tool is providing protected. However, in RoboMongo, it is tough to see that, of course. It's better at one thousand or four thousand since in a single row.
For how long have I used the solution?
I have experience with this product. I've used MongoDB intermittently since around 2020. It's a deep system. You need to find the data, and sometimes use queries. There's a conversion tool that helps transform static queries into MongoDB 3 format.
What do I think about the stability of the solution?
In RDBMS, we have an option to put triggers and functions in the database. In MySQL, you have an option to put a function as well as a trigger, but I haven't found that option in MongoDB. I can create functions, but I am not able to create a function trigger there. We have to create, get, update, and delete, which I can do in MySQL and SQL Server also. But in the same way, you can perform in MongoDB. That is the only thing I noticed.
Other than that, the query that is performing, creating, updating, and deleting everything can be made possible in MongoDB. You cannot create only that trigger in MongoDB. I haven't found anywhere to create that trigger.
Without triggers, you can't automatically execute actions in response to data changes in MongoDB.
That is the only drawback that I find with MongoDB: creating the trigger. Apart from that, I think everything can be possible. We can put function software into the database, and you can execute the review. But when creating the triggers, you need to perform separate functions for that.
What do I think about the scalability of the solution?
Scalability in MongoDB always depends upon the configuration. We can configure it accordingly, but it is definitely essential.
Your hardware system should also ensure the number of resources your application is consuming. For example, in my application, if the unit is more than 400 kits. In a point of time, it will get executed.
I tested that using JMeter. When I'm doing the amenity services, I always put the 500 resource at a time. At a single point of time, 500 resources will get in that 5, so I just find any issues on that. The client also has not had any issues.
When we are doing any of the microservices, we need to ensure using JMeter. Via JMeter, you can ensure, like, how much on the port of ten, how much in the source can be accessible.
How was the initial setup?
It's a one-click install. Maybe, like, two settings. If you already have MongoDB, five to ten minutes is regarding some MongoDB. The only thing that you should know is the port number and the IP address if you're exposing your application to a third party. I think if you're aware of those risks, you can install it immediately. It's easy if you need to collect that data. You might know five to ten minutes.
We can install the remote engineer system. I don't think it will be a bigger task, but even if you're configuring for multiple people, you just need to add that particular port number in your system. Otherwise, it won't allow you to log in.
Even if you're using Microsoft authentication, we normally have multiple layers of authentication. So use the command password, and then you will get the notifications, whether you are getting log-in or not. That will take some time.
Maintenance:
For getting queries only, we put a Java set. From the development perspective, once the database is set up and you configure the URLs, everything works fine. You have 192.138.1.1 URL, it automatically connects to the review if the network is enabled. Then it connects to the review. However, it definitely depends on the bus service we are passing. It should work fine with no issues if the configuration is okay.
That is how we install it. Once we have source, then it's the same network. If it is on the same network, we have a contract, the traffic is there, and the agent works.
If I want to test whether my microservices work fine, I use them again, and they test if my microservices are working fine. Normally, almost all microservices are in a rack server, so you don't get the performance there. I haven't found any issues directly.
What other advice do I have?
If you are looking for a robust system with a lot of security concerns, then you should go for IBM. I'm not saying MongoDB is not 100% secure, but for highly confidential data, I would suggest other solutions.
However, in MongoDB, you can do filing processes and vertical reports. Everything can be done in MongoDB, but the newest is a relationship. You cannot maintain the referential integrity relationship. You can maintain it, but it will be a little tough.
If you want to maintain the relational database in MongoDB, the resource should be at least a minimum of one and a half years highly exposed in MongoDB. Then only we'll be able to manage that data. Even for new joiners, it is a little tough to explain how the relational database is maintained in MongoDB.
Overall, I would rate the solution an eight out of ten.
Easy to use, flexible to changes, and performs well
What is our primary use case?
The application we are working on is built on MongoDB.
What is most valuable?
MongoDB is a NoSQL tool. We can easily add fields. It provides more flexibility to store data. It is flexible to changes. I have not encountered any performance issues.
What needs improvement?
Searching and browsing through the collection must be made easier.
For how long have I used the solution?
I have been using the solution for two years.
What do I think about the stability of the solution?
The product has been stable so far.
How was the initial setup?
The installation was easy. The deployment took an hour. One person is enough to deploy the tool. It does not require much maintenance.
What's my experience with pricing, setup cost, and licensing?
I am using the free version of the solution.
Which other solutions did I evaluate?
I have used DynamoDB before. MongoDB’s free version is quite good for our use cases. DynamoDB is expensive.
What other advice do I have?
MongoDB is a very good tool for first-time users. Overall, I rate the solution an eight out of ten.
Using MongoDB: Leveraging Capabilities While Handling Obstacles
What do you like best about the product?
A document-oriented data architecture is used by the adaptable and scalable MongoDB database, which enables dynamic schemas and simple changes to document structure. Comprising grouping, filtering, and converting data is made possible by its strong query language and indexing features. Because MongoDB uses sharding—a horizontal scalability technique that divides data across numerous servers—it is exceptionally well-suited for managing large-scale data and high-throughput applications. Data redundancy and failover are ensured by its built-in replication and high availability features, which also minimize downtime and guarantee data integrity. All things considered, MongoDB is a useful option for contemporary, data-intensive applications.
What do you dislike about the product?
When compared to traditional databases, its implementation can be less resource-efficient and straightforward, which makes it challenging to manage scenarios needing complicated transactional consistency across several documents. Furthermore, inconsistent data in sharded clusters might result in performance bottlenecks and unequal data distribution. Because of MongoDB's flexible design, inconsistent data structures may result, which could affect the quality of the data and complicate application and query logic. For applications demanding higher consistency assurances, MongoDB's default setup for Write Concerns and Read Concerns may not be ideal. Careful customization is therefore necessary. To overcome these obstacles and capitalize on MongoDB's advantages while minimizing its drawbacks, meticulous preparation and knowledge are needed.
What problems is the product solving and how is that benefiting you?
To create data-intensive applications, like IoT storage solutions and social media analytics systems. Because of its document-oriented data format, changing data requirements may be accommodated without requiring significant schema migrations, allowing for agile development and iteration. Due to its scalability, MongoDB can easily expand without compromising speed, making it an ideal solution for managing large datasets and high-throughput applications. Consulting businesses may create complex analytics and reporting solutions by utilizing its advanced querying and indexing capabilities to pull important insights from a variety of datasets. These functionalities are essential for creating data visualization apps, business intelligence tools, and custom reporting dashboards that are suited to the individual requirements of clients.
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