MongoDB Atlas Reviews

We're developing a product using multi-tenant architecture, but we don't have any predefined structure, so we need to use MongoDB Atlas to support predefined architecture.

Being schemaless is what I like best about MongoDB Atlas.

An area for improvement in MongoDB Atlas is that it does not support individual or personal database backup, though it supports cloud cluster backup.

I want a query feature added to MongoDB Atlas, or if it's available, improve on it. My team needs manual coding for the pipelines, for example, creating and executing pipelines. If the query feature of MongoDB Atlas has some improvement, then the process for pipeline creation and execution would be better.

I've been using MongoDB Atlas for around three years.

MongoDB Atlas is a stable solution. A product related to it, the MongoDB Atlas Data Lake, on the other hand, could be more stable.

My company contacted the MongoDB Atlas Australian support team once, and I'm rating the team eight out of ten.

We decided to go with MongoDB Atlas because you also get MongoDB Realm, a product that lets you sync data with mobile devices.

MongoDB Atlas has an easy setup.

I didn't deploy the solution because another senior took care of the deployment.

I'm not familiar with the cost of MongoDB Atlas.

I'm using MongoDB Atlas version 4.2.

The solution is deployed on the MongoDB cloud.

Around four people, mainly developers and techs, use MongoDB Atlas within the company.

I recommend MongoDB Atlas to others because of the support my company gets, apart from the product being schemaless. MongoDB Atlas also has other features, and you can take advantage of MongoDB Realm, so it's like getting more than one product.

My rating for MongoDB Atlas is eight out of ten.

My company is a MongoDB Atlas customer.

I'm no longer working for the company that uses MongoDB Atlas, and I didn't stay there long, but the search functionality of the solution was helpful. The cost reduction from using MongoDB Atlas has also been beneficial. The solution also supports pay-as-you-go, another benefit of MongoDB Atlas.

What I found most valuable in MongoDB Atlas is its Elasticsearch feature.

MongoDB Atlas would be better if it had facilities for data warehousing, data lake, or ETL jobs. It probably has this functionality for large data sets, but I've not read about it, and I'm not so sure.

It would also be great if it were easier to integrate MongoDB Atlas with AWS services. Native integration between MongoDB Atlas and AWS services would make the solution better.

In the next release of the solution, the company wants to receive better support from the MongoDB Atlas team.

I used MongoDB Atlas for eight to twelve months, but my last usage of the solution was six months ago.

MongoDB Atlas has high availability, so I have no complaints about its stability. When the database goes down, it results from huge queries or some malfunction in the operations, but the solution is usually stable.

Regarding scalability, if I'm running the free version of MongoDB Atlas, a tiny application, it's an eight out of ten. However, if I'm running an extensive application that requires some support, I'd rate the scalability of MongoDB Atlas as six out of ten.

I keep hearing from my previous team that the technical support for MongoDB Atlas has been slow on some queries or query executions.

You also have to pay for MongoDB Atlas support, and it's expensive. It would be great if support were cheaper because the team frequently interacts with technical support because of database failures, connection breaks, etc.

The technical support for MongoDB Atlas is not up to the mark, mainly because of the extra costs, so I'm rating that area three out of ten.

Negative

My company previously used Amazon DocumentDB, so I contacted the MongoDB Atlas team as I thought it would be better for the company to switch to MongoDB Atlas because it offered Elasticsearch, which would be helpful for the company's use case. Instead of setting up a separate Elasticsearch, my company can use the Elasticsearch functionality in MongoDB Atlas.

Another reason for switching to MongoDB Atlas is that its cost was significantly less than the cost of Amazon DocumentDB.

Amazon DocumentDB lacked some of the features you can find in MongoDB Atlas because it wasn't up-to-date with the latest MongoDB.

MongoDB also supported query execution, so it was better for the use case of my company.

It is pretty simple to set up MongoDB Atlas from scratch, and it's a good experience, but migrating from another solution to MongoDB Atlas is more complex. For example, the company was using Amazon DocumentDB and had to migrate from that solution to MongoDB Atlas. Because of the massive data, it took some time to migrate to MongoDB Atlas fully.

Deploying the solution required the support of the MongoDB Atlas team. Still, if you have one person with expertise on the infrastructure, for example, a DevOps person, that would suffice, primarily if your data volume isn't that big.

If it's a raw MongoDB Atlas setup, one person with basic skills can also handle the deployment by learning the ready-made setup process from YouTube. However, if you want to set the solution up with your EC2 instance sitting around in some VPC, you must do VPC pairing.

Connecting MongoDB Atlas with AWS Lambda on a VPC will be more challenging because it's outside the AWS native environment. Connecting the server to the MongoDB Atlas database would be a challenge. The deployment team would need various skill sets for challenging tasks, such as database migration, VPC pairing, etc.

An in-house team implemented MongoDB Atlas with a consultant from the MongoDB Atlas team.

I don't have many complaints about MongoDB Atlas, ROI-wise. It's just the support that's a little expensive, so on that side, there's not a very great ROI. However, from the database side, as MongoDB Atlas has many new features coming in, the ROI is okay.

MongoDB Atlas is more cost-effective than Amazon DocumentDB. It also has a pay-as-you-go pricing model.

Apart from the standard licensing cost, you must also pay to get MongoDB Atlas technical support, which is expensive.

MongoDB Atlas has different pricing models, so pricing is five out of ten for storing the data. The support is expensive, so that's three out of ten. For data updates, I'd give the price seven out of ten.

I have some experience with MongoDB Atlas.

I remember using version 4.2 of MongoDB Atlas.

I advise anyone planning to implement MongoDB Atlas to start small and get around the platform and documentation. Learn about the MongoDB Atlas fundamentals, such as VPC pairing, etc. If you want to scale the solution, review the documentation on that first, apart from learning the platform.

My rating for MongoDB Atlas is seven out of ten.

I manage services and keep them running. The business analyzes the social media data with machine learning, and that data is stored in MongoDB.

Administering the solution is easy.

The administration is not very interactive. Most of the time, you don't need to interact with the database, just create a user and indexes. It's not very friendly for developers.

Based on its own habitat, it's not ACID compliant. If it had an ACID compliant option, it would be more useful for database administration.

I have been using this solution for almost three years.

It's a stable solution.

I think there are two kinds of support. One of them is local. If there is any trouble, we open the ticket with a local vendor. If they can't solve those problems, they escalate the problem to the level three engineers at MongoDB.

I would rate their technical support 4 out of 5.

I have also used Microsoft SQL databases. There's a distinction between SQL and MongoDB because one of them is based on the schema first. The other is not schema, not permanent, because of ACID compliant databases. The transaction is very important at the SQL, MYSQL, and Postgre databases, so if any transaction fails, it has to roll back because of the schema at most of ACID compliant databases. 

There are 35 million customers that consume those databases, like a POC system. Lots of transactions passing through the system are based on Microsoft SQL. Only semi-structured data – like a document based system like Twitter and social media data – is based on MongoDB.

There are two kinds of setup. One of those setups is sharding, and there is replication. The other setup is hybrid. It's not hard to implement those systems.

Setup is easy. There are only a few commands that you have to execute. First of all, the design is a very important part of it. If you design it based on the best practices, it's easy.

As a database administrator, all those things come around as ready to run. You only need to log in to the server and implement SQL, Mongo, and MySQL databases to implement. It takes only half a day to implement those.

We had a lot of documentation. Most of the time, we couldn't expand those rules, but it's easy to implement. 

We have almost 11 people on the database team.

Comparing the price between the MongoDB and Microsoft SQL Server, we are using the enterprise edition of Microsoft SQL Server, which is more expensive than MongoDB.

I would rate the price of MongoDB 4 out of 5.

I would rate this solution 10 out of 10.

It's a solid product. A lot of issues have been resolved. It's very sustainable, and it takes minimal effort to solve a problem. It's very easy to deploy.

We use MongoDB Atlas to store the data of our users.

The product allows us to easily set up and store large amounts of unstructured data.

The product's data aggregation feature needs to work faster.

We have been using MongoDB Atlas for one year.

I rate the product's stability a nine out of ten. It is a good feature that is rarely seen in other relational databases.

I rate the platform's scalability a nine out of ten.

The initial setup process is easy.

It is an open-source platform.

If you have unstructured data in large amounts, MongoDB Atlas is a good product to opt for. It distributes the data on servers and gives quick access to it.

I rate it a nine out of ten.

MongoDB Atlas can be used to store data, migrate data from SQL to NoSQL, and collect data from websites.

Object-based data storing capability and managing non-structured data capability are the most valuable features of MongoDB Atlas.

MongoDB Atlas should add more APIs in their Terraform module because sometimes I find it difficult to find the resources in their Terraform model.

I have been using MongoDB Atlas for two to four years.

I rate MongoDB Atlas a nine out of ten for stability.

I rate MongoDB Atlas ten out of ten for scalability.

MongoDB Atlas’ initial setup was straightforward.

We use Terraform to deploy MongoDB Atlas. The deployment is easy if you have the code, and can be done within five minutes.

MongoDB Atlas has a good partnership with AWS, and there is no hassle in integrating the solution with any tool. I recommend users go ahead with MongoDB Atlas if they prefer to have NoSQL in their organization.

Overall, I rate MongoDB Atlas ten out of ten.

I am impressed with the tool's integrations.

The tool's implementation should be made easier.

I have been working with the product for two years.

The solution is stable.

The tool is scalable. My company has more than 40 users for the solution.

We had mailed tech support whenever we encountered issues and they had responded to us.

The solution's deployment can be done in-house.

The tool is free since it's an open-source product.

I would rate the product an eight out of ten.

I have used MongoDB Atlas for my personal side projects as a database, and I have also used it as an infrastructure-as-code (IAC). In my apps, I have used the API to manage and create new databases programmatically. At my workplace, we use MongoDB Atlas for database management, access management, and configuration using Microsoft.

The key feature of MongoDB Atlas that has been helpful for us is the ease of deploying new databases. As a SaaS product, we don't need to worry about the infrastructure for on-premises servers or maintaining them. Additionally, MongoDB Atlas allows us to connect to our cloud, whether it's Azure, GCP, or AWS. This means we can manage these resources within our own cloud environment, change security rules, and integrate with other cloud resources as needed.

There are more than one hundred users in my organization.

I find the cluster demonstration and APIs to be the most valuable features of MongoDB Atlas. The APIs allow me to programmatically make changes to the environment. Additionally, the interface is user-friendly and easy to navigate. Another feature that I find useful is the separation of projects, which allows for different projects to have their own settings.

One improvement that I would like to see is a feature to export changes made in the environment, such as creating a new user. MongoDB Atlas should provide a way to export this action programmatically, so that we can easily integrate it with our IAC tools and deploy it afterwards.

I have been working with MongoDB for around six years now, and have been using MongoDB Atlas since it was launched. I believe it's been about two years since I started using Atlas, but I can't recall the exact time.

It is a very stable solution.

MongoDB Atlas is scalable and it offers several features for a DB as a service.

We have used Azure SQL in the past, but it's not the main use in projects in the organization. We have also used MongoDB Atlas for their project, and Oracle DB is used in the organization as well.

The initial setup was very easy. We didn’t need any expert to set up the solution.

For my personal projects, I mainly use the UI interface of MongoDB Atlas, as well as the APIs. I write code that connects to the APIs and makes changes to the environment, such as creating new databases and managing security settings. In an enterprise setting, we typically have an administrator who uses the user interface to manage the environment, so there's not as much need for a specific deployment process.

There is some ROI because we don't need to have a separate operations team to maintain servers physically. MongoDB Atlas eliminates the need to worry about physical things like energy and space, as they are managed by the cloud provider.

It has a fair price.

I prefer GCP because it's free and has great features related to artificial intelligence and the network is also very good. However, Azure has the best interface that is easy to use and deploy. It has ERM templates, which are JSON templates used to define resources that can be deployed, making it easier to work with. In contrast, GCP lacks such templates and some APIs are not easy to use.

I've had a very positive experience using MongoDB Atlas so far. It's easy to use, and scalable, and the cloud-based option is very convenient. Overall, I would definitely recommend it to others looking for a database solution.

I would advise learning some SQL concepts to use MongoDB Atlas correctly. MongoDB Atlas is a cloud-based platform that provides a managed database service for MongoDB, a NoSQL database. Additionally, it is important to have network knowledge, specifically for administrators.

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

We create a MongoDB database or clusters. Then, we connect it to the application in a normal simple way.

The dynamic structures are the most valuable. The SQL is fast but requires a different structure.

The speed when combining two documents is concerning. In SQL, there are Joins. You can define relationships between two tables and go for complicated structures, but you can't do that for MongoDB Atlas. You can have logical structures that do the same thing, but they are really slow compared to the Joins in SQL. Combining two separate documents within a database is not recommended because it affects performance. So if they can make it faster and more efficient, it would be amazing.

We have been using this solution for 15 months. I believe it is deployed on public cloud, AWS.

It is a stable solution. 

It is scalable, and they provide different options. We have about 20 people using the solution in our organization, and they are mostly developers.

The initial setup was straightforward.

I believe when it is deployed publicly, it is pretty cheap. But the costs are high if it is deployed on-premises or you're using a private database.

We decided to use MongoDB Atlas because we required some dynamic data structures. We never wanted to stick with a well-defined one. When there were questions about using well-defined ones, PostgreSQL turned out to be one of the choices, but we chose MongoDB Atlas.

I rate the solution an eight out of ten because it's a good product. It supports many functionalities, is easier to implement, and the only issue is speed. It also doesn't have a lot of features that could support Joins. So if they fix that, I could scale my rating as high as nine or ten. I would recommend the solution. It is very developer-friendly, and the learning curve is not that high.