I primarily use Oracle databases, but I work with many other databases such as MongoDB Atlas and several cloud databases. I utilize MongoDB Atlas predominantly for training-level projects in resource grooming and for sub-projects at my office. It is used alongside Oracle and Postgres in these training layers.

MongoDB Atlas offers replication, which is cheaper than Oracle RAC, making it appealing to certain industries. It is particularly useful for unstructured and semi-structured data because of its performance in these areas. Sharding and partitioning are supported, though they don't reach the same level as Oracle's capabilities. This cost-effective solution assists organizations in data storage and management.

It would be beneficial if MongoDB Atlas could better support OLTP aspects and data frames, as well as enhance its capabilities for data pipelines and visualization dashboards. Furthermore, supporting the medallion architecture could be a valuable addition, and incorporating improved spatial and vector handling for geographical data could make it more competitive. Enhancing vector processing for AI capabilities would also be critical.

MongoDB Atlas is effective for unstructured and semi-structured data, but when it comes to OLTP transactions, its performance declines. This is a continuous challenge I face when utilizing MongoDB Atlas.

MongoDB Atlas offers sharding as a scalability feature, although it does not perform as well as Oracle. Partitioning is also available; however, it lacks a multi-tenancy architecture, which affects its scalability in comparison.

Technical support from MongoDB Atlas, which is open source, is satisfactory in most cases. However, when compared to top databases like EDB, Postgres, and Oracle, the features of MongoDB Atlas fall short, resulting in an average rating due to higher-expectation features still lacking in its offerings.

The price of MongoDB Atlas is reasonable, which is why many organizations, including mine, are opting for it.

The overall rating for MongoDB Atlas is around 5.5. To improve, MongoDB should enhance support for demanding graph databases, vector databases, and spatial handling. Additionally, improvements in AI capabilities, particularly vector processing, are imperative. These developments could provide MongoDB Atlas with a competitive edge.

We may use it as an application database. The application stores the data as documents in the database, which is a preference for our company because it’s a Document DB and a NoSQL database, which are preferred over traditional relational databases.

MongoDB has wrapped up the whole development lifecycle. MongoDB has multiple built-in tools such as MongoDB Shell, Compass, and other tools. It helps the developers to use that specific tool efficiently. Users do not have to worry about finding the tools and then installing and using that specific tool to communicate with their database cluster. MongoDB has a built-in option using MongoDB Shell or Compass for that purpose.

So, it has positively impacted the development speed and productivity.

There are many valuable features, but scalability stands out. It can scale across zones. You can define multiple nodes. They have also partnered with AWS, offering great service with multiple features, including built-in backup, all under the same roof, without the need for external tools.

So, the scalability feature supported our data growth overall. The growth of the database depends on the application side. The database aids in scaling when the application requires more storage. 

It’s configured to scale automatically across zones and regions, ensuring that performance doesn’t degrade even when scaling down.

The scalability aspect is quite difficult to implement. It should be much easier for the end user. You cannot use less than two nodes; you have to use at least two nodes, and they categorize their nodes, like m5, m10, and m20, according to their resource practices, which are also a bit expensive.

The end-user has to learn a bit about it. MongoDB has great content on its site. They call it MongoDB University. They actually have great content for that. Anyone can learn it, but one has to study it before diving into it or starting to use it.

I have been using MongoDB Atlas for almost three years.

The scalability is good. In my team, almost the whole development team is using it. So, there are around five end users. 

I contacted customer service and support for multiple purposes while configuring. The support is quite efficient, and the guidance is quite good. Initially, when I was working on it, I had to communicate with the support team.

So, I had a good experience with the support. 

Positive

The initial setup is not too difficult but can be somewhat tricky. 

It is tricky mainly in terms of configuration, especially if it's not internet-accessible, configuring it to stay within the same data center while allowing developers access without network barriers.

It is worth my money at the end of the day.

The pricing is not that expensive, but it can be, especially when we have deployed it across multiple zones.

If you want to go with NoSQL, I would suggest using MongoDB. 

If you are saving documents and prefer AWS services, AWS also has their DynamoDB for that purpose. I would suggest using AWS service if all of your services are already on AWS.

Overall, I would rate it a seven out of ten.

In my SaaS role, MongoDB Atlas helped me meet compliance standards by keeping tenant and customer data in separate databases. I improved performance by setting up Atlas clusters in each region. In a high-restriction context, data replication between regions ensured smooth failover without constant snapshots. For added security, I even took data snapshots to another cloud. Choosing MongoDB Atlas depends on your specific SLA and disaster recovery needs.

The most beneficial MongoDB features for our workload are the ability to scale up and down using automatic sharding and clustering. This is crucial for handling diverse requirements and integrations with different configurations. The flexibility to support various deployments, whether on AWS, Azure, or GCP, by easily adding additional clusters, has been another significant advantage.

There is room for improvement in the cost of certain features like encryption. The web console isn't very intuitive, especially for large data. The new feature, Cube, for BI needs better documentation, and more workshops or videos would be helpful for users and developers. Additionally, real-time performance monitoring in MongoDB Atlas needs improvement.

I have been working with MongoDB Atlas for about two months, but my company has been using it for a couple of years.

MongoDB is quite stable.

The technical support is very good. I would rate it as an eight out of ten.

Positive

I have worked with MySQL and PostgreSQL. MongoDB stands out for managing JSON and document types most efficiently. While other databases support JSON fields, MongoDB offers the best solution for handling JSON data.

The deployment was quite straightforward and it only took about half a day. 
Maintenance is generally manageable with MongoDB, but it becomes challenging when facing issues or disasters. The profiler and logs are not very user-friendly which makes it harder to identify and resolve problems quickly.

The ROI with MongoDB Atlas depends on how well you manage your data and throughput. It is crucial to understand your data and use their tools effectively. Mismanagement can lead to higher costs, especially with scaling, so careful monitoring is essential.

I don't have any concerns about MongoDB Atlas pricing and licensing.

MongoDB's default encryption provides a baseline for data protection. The flexibility to let customers manage their encryption keys enhances security.

MongoDB Atlas supports our organization's implementation of microservices architecture by seamlessly fitting into our domain-driven design. While we primarily use domain-driven design rather than microservices, MongoDB Atlas offers flexibility and scalability to meet various requirements. It easily accommodates diverse data structures and events related to our customers, allowing for efficient and straightforward implementation.

Overall, I would rate MongoDB Atlas as a seven out of ten.

My advice to new users is to design your schema thoughtfully, considering MongoDB's document limitations. Utilize profiling and set up alerts for scaling issues. Understand how to query efficiently and focus on proper indexing for improved performance.