We primarily utilize MongoDB Atlas for tasks such as IoT integration. Additionally, it serves as a general-purpose database that aggregates analytics data before transferring it to a data lake. Its versatility allows for various applications, providing flexibility and ensuring the availability of essential data across different systems. While it is used in diverse contexts, many use it for IoT-related initiatives.

We prefer MongoDB Atlas over SQL because most of the data generated with IoT devices is unstructured. This gives you flexibility; you don't have to define specific schemas all the time, and sometimes, the structure of the object varies.

It improves data management along the same lines. MongoDB Atlas supports structured data with IoT projects.

MongoDB Atlas was explicitly designed to support IoT applications. Many databases offer features tailored for IoT use cases.

One area for enhancement is containerization. They could explore ways to facilitate deploying MongoDB containers within the platform.

I have been using MongoDB Atlas for five years.

I rate the solution’s stability a nine out of ten.

Two people use this solution because they work with sensors and other variations of IoT.
I rate the solution’s scalability a nine out of ten.

The tool provides a forum where users can engage with experts. These experts offer assistance tailored to your specific needs, whether you're focused on product-centric queries or diving deep into particular use cases. Ultimately, the support you receive depends on your requirements and the extent of your experience with the platform.

The initial setup of MongoDB Atlas is straightforward. The user-friendly UI guides you through the setup process seamlessly. It would be beneficial if they could maintain this simplicity across different operating systems. Additionally, if they can streamline the process to easily deploy with containers, it would greatly enhance user experience and make life easier.

MongoDB Atlas offers various options based on your needs. It can accommodate both, whether you require the enterprise version with advanced features or prefer to start with an open trial version.

Security is primarily organized around organizational principles, allowing you to customize and adjust each tool according to your specific security policies. I recommend the product. Every product serves a purpose as long as it addresses the right problem. MongoDB Atlas has proven particularly effective for applications such as analytics and IoT, making it a recommended choice for those use cases.

Overall, I rate the solution a nine out of ten.

We restore our golden data from various sources and then push it to MongoDB. We make our CDP from MongoDB, which serves as a device-centric system.

There is a built-in feature called Autoscaling In MongoDB Atlas. This feature automatically adjusts the configuration of MongoDB based on the volume of users we ingest daily. Autoscaling dynamically scales the resources to accommodate the load when our data flow increases.

The real-time data visible within MongoDB Atlas is not accurate. If they can improve the UI that monitors real-time data. It's more impressive and more attractive. It could be more user-friendly.

I have been using MongoDB Atlas for two years.

The product is pretty stable.

The solution is scalable. Autoscaling supports it.

50 users are using this solution

Whenever we have doubts during configuration, we reach out for assistance. We must upgrade certain parameters in our MongoDB setup, prompting us to contact their support team. They resolve such issues within four to five hours.

The initial setup is not very complex. It is easy to use. It's easy to deploy on MongoDB. We push from GitHub. From there, we specify where the data is restored in MongoDB. We continue to connect. It puts the data and delivers it to Argo City.

The product has a yearly subscription.

We have assigned DevOps for security.

The overview and monitoring part will address this issue, and then we will use it to observe any increasing traffic on our website. We also monitor the rising number of connections due to this traffic. It's quite easy to oversee everything in one place. However, the UI isn't particularly user-friendly.

I've also used it in my previous company and found it handy and easy to configure, including easy capabilities.

We are establishing SLAs that are directly tied to MongoDB. All are interconnected with MongoDB. If MongoDB experiences downtime or RAM or CPU usage spikes significantly, users may encounter difficulties logging in. This reliance on MongoDB can pose challenges for user accessibility, particularly when considering the conferencing tools we use.

Overall, I rate the solution an eight out of ten.

We use it in a cloud setup on Google Cloud Platform as part of a microservices-based cloud solution. These microservices communicate with messages, and one use case for MongoDB is storing specific messages we're interested in.

MongoDB has supported our organization's need for scalable and flexible data storage.

We use it internally, where different teams manage different microservices. Sometimes, internal incidents arise, requiring teams to dedicate personnel to resolve and communicate with other teams.

With MongoDB, other teams can now access some of our data and investigate issues on their own, freeing up personnel for other tasks.

Moreover, this solution simplifies real-time data analytics or application development for our business.

It simplifies things by automating previously manual tasks. It acts as a self-service portal for our team, reducing manual work and enabling automation.

We're happy with the performance, maintenance, and especially the ease of use within Google Cloud.

Given our microservices architecture, it's like a large puzzle, and MongoDB feels like it fills the gaps we were facing. So, the global clusters feature has enhanced our application performance and user experience.

It helps us optimize team performance, which is valuable.

The initial configuration could be a bit easier.

I have been using this solution for a couple of years.

We've experienced some issues, but most MongoDB issues are resolved quickly. The issues we face are mainly with other systems.

So, it is a stable solution.

It is a scalable solution because we use quite a lot of data, and it handles it well.

It's a microservice solution, so each microservice runs on several pods, maybe eight. Each pod uses MongoDB and makes its own connections, so multiply by eight, maybe 100, so roughly a thousand users.

These are internal users, so we're fine with the current number.

MongoDB offers free support online, and they seem to be doing a good job overall.

We have used other databases as well, including Google Cloud, for the past two years on our current project. My company policy guides such decisions. Overall, the company is happy with MongoDB.

The setup is automated through our partner using Terraform for provisioning, not just for MongoDB but for our whole infrastructure. We manage daily deployments using TerraForm, and MongoDB setup on Google Cloud is very smooth.

The deployment is very quick. For example, microservices using MongoDB start very quickly, possibly within a minute.

We haven't had major issues with deployment or configuration. Maybe initial configuration fine-tuning for performance can be time-consuming, but the initial effort pays off later with reduced maintenance needs.

Expertise in automation and deployment processes is helpful and worth learning within the team.

We do it in-house. It's integrated with Google Cloud, GitHub, and GitLab actions. Everything is cloud-based and easy to work with. It's been continually improving over the years.

We don't use external consultants, as we have in-house expertise. It's a 100% cloud solution.

We don't have engineers dedicated to maintenance. It's part of our continuous integration and delivery environment, so there's not much manual intervention needed. Issues usually arise when deploying incorrectly and rolling back, but deployment itself is straightforward.

In some teams, companies, and projects, there might be two to three people dedicated to everything, which is a lot. If these skills to analyze productivity or cost saving can be automated, these people can teach others and do more valuable work. It's all win-win.

The price is cheap enough. It is comparable and has average pricing. We have a long-term license.

The pricing is acceptable for enterprise tier.

We haven't faced any major issues so I would rate this solution a nine out of ten.

In this project, it's more integrated than previous ones. The level of integration, automation, and evolution is impressive when used well. It's flawless, straightforward, and hassle-free.

It's good for performance and stability if you need a non-SQL database to store data.

We use it as a database for some of our microservices. We use it as a database for a few of our microservices.

The stability and performance are great. The high availability feature is great.

Moreover, I am happy with the automated backup and restore functionality.

In the past, MongoDB offered more features for free, but now it's quite limited. The free version is limited, and you need to pay extra to fully utilize it.

The pricing could be improved.

I have experience with this solution. I've been with this product for a couple of years.

It is a stable solution. I would rate the stability a nine out of ten.

It is a scalable product, but only if you use the paid features. And if you enable sharded cluster functionality, it scales very well.

The initial setup is very straightforward.

The ease of setting up and maintaining your database clusters with MongoDB depends on the features you need. If you only need basic functionality, setup can be simple. But for additional features like reliability and backups, it might require a more complex configuration.

We did it in-house.

Overall, I would rate the solution a nine out of ten. I would recommend using this product.

If you need a no-SQL database, then MongoDB is a good choice.