Microsoft Azure Cosmos DB Reviews

In my role, I use Microsoft Azure Cosmos DB fairly extensively across various platforms. At ASOS, we utilize it for order processing to record incoming orders and for commercial integration platforms. Overall, we have numerous use cases.

Overall, Microsoft Azure Cosmos DB is easy to use.

Microsoft Azure Cosmos DB has provided benefits compared to SQL databases, particularly in terms of availability.

Microsoft Azure Cosmos DB has helped to improve our total cost of ownership.

Microsoft Azure Cosmos DB offers a relatively easy learning curve due to its limited programming service area compared to SQL Server. This streamlined functionality allows users to quickly grasp the SQL query language.

The autoscale feature is the most useful for us.

There should be parity between the various APIs. I often work with the Mongo API, and features for it sometimes lag substantially behind the core API, such as the Analytical Store feature. Additionally, I am waiting for the full fidelity change feed that would surface all changes, including deletes to documents.

While Microsoft Azure Cosmos DB is generally easy to use, it has some limitations. Certain areas are more restrictive, and we are awaiting features that will simplify development. For example, currently under development, the full fidelity change feed will expose all document changes, enabling tasks like synchronizing collections while accounting for deletions. This is challenging because the existing change feed doesn't provide information about deleted documents.

I have been using Microsoft Azure Cosmos DB for around seven years.

Cosmos DB demonstrates good stability and great reliability, with technical issues arising approximately once per year.

While Cosmos DB offers good latency and availability, careful consideration must be given to selecting appropriate consistency levels.

The scalability is excellent, and as long as the data can be partitioned, the scalability is nearly infinite.

Cosmos DB's ability to scale workloads is a significant advantage, as evidenced by our successful management of multiple terabytes of data without encountering any issues.

The quality of customer service and support varies. We always get an answer eventually, but the speed of resolution depends on the reason for the support ticket. If there is a bug in the product, we have to wait for it to be fixed.

Positive

The initial setup of Microsoft Azure Cosmos DB is straightforward. Even someone with no experience can easily deploy the solution.

The deployment can be completed by one person on the same day.

The deployment can be done entirely in-house. Whether we are doing it manually in the portal or deploying it through Terraform, it is straightforward. We do not require help from an integrator or consultant, although there are considerations about partitioning collections when creating resources.

The pricing for Cosmos DB has improved, particularly with the new pricing for Autoscale. Previously, we were charged according to the busiest partition across all regions, but now, each partition is only charged for what it uses. This change has substantially reduced our costs.

When evaluating new projects, we determine whether data storage is best suited for a relational database, such as Azure SQL Database, or a non-relational database like Cosmos DB.

I would rate Microsoft Azure Cosmos DB eight out of ten.

Understanding some of the subtleties of Microsoft Azure Cosmos DB can take time, and some individuals at ASOS still find concepts like partitioning unclear. However, getting started with Cosmos DB and developing functional applications is quick and can be achieved in a short timeframe.

We require minimal maintenance to validate that we've configured, for example, the correct indexing policies as required by our queries.

New users should make sure they understand partitioning because once it's selected, it is difficult to change it. Otherwise, you would need to migrate everything over to another collection.

I like to describe it as a programmer's database. .NET developers, in particular, can design and work with the data easily because it's schema-free. Unlike traditional databases, which are considered rigid with their rules, developers really love Cosmos DB because of its schema-free nature and the freedom it offers.

Cosmos is widely used for web applications. You can also use it for inventory management and IoT solutions... there are a ton of different applications.

It's very easy to integrate Azure Cosmos DB with other Azure services. For example, generating a Power BI report from data in Cosmos is just a few clicks. It's also simple to stream IoT or sensor data into Cosmos.

When it comes to supporting IoT or real-time analytics, the main advantage is speed. Cosmos DB doesn't rely on traditional disks and database structures like a relational database. It uses JSON, which is similar to XML, and that makes it incredibly fast.

The way it was designed is most valuable for global distribution. Unlike old-school SQL Server that was intended for a single data center, Cosmos was built from the ground up for global availability. 

Features like geo-clustering and mirroring were not afterthoughts. If you have a database in Chicago, you can right-click and easily create a failover group in Japan. That works well for global companies with offices across continents; it minimizes latency.

Cosmos's multi-model support made databases more highly available. 

The downside is that Cosmos is new and fairly complex. There's a limited pool of talent who are really good at working with it.

Because of that, I've been approached by recruiters quite a bit; they see my Cosmos DB certification on LinkedIn. It's hard to find people to work on Cosmos projects. 

Sometimes, a really smart developer will design and build a Cosmos implementation and then move on, leaving the company struggling to find someone to work with it and maintain it.

Interestingly, if you need to restore a Cosmos DB database, you have to put in a ticket with Microsoft – they're the only ones who can do that.

I've worked with Cosmos on and off for about three years.

For Cosmos DB, their technical support is very good. They are the experts in that product.

Overall, the customer service and support are excellent. 

Positive

I did a couple days of training on DynamoDB, which is Amazon's comparable product to Cosmos DB.

They're actually quite similar, both being multi-model databases. Relational databases are good for structured data, but once you get into semi-structured and unstructured data, they just don't perform well. 

That's where DynamoDB and Cosmos DB excel – storing, indexing, and quickly working with that less-structured data.

The initial setup is simple and straightforward. You can set up a Cosmos DB in a day, even configuring things like availability zones around the world. 

The harder part is on the developer side – designing the collections (similar to tables) and how the data will flow in.

I've set up a few Cosmos DB instances, and it's about a half-hour tops.

One person can handle the deployment. I'd typically set it up alongside other Azure components like a VM. You choose your settings, networking details, etc., basically walk through a wizard, hit deploy, and it's up within half an hour.

There are some configuration options for database administration on the customer's side. You'll need to go in and enable things like automatic indexing with checkboxes.

With heavy use, like a large-scale IoT implementation, you could easily hit a quarter of a million dollars a month in Azure charges if Cosmos DB is a big part of it.

I would recommend using it, but with a caveat – it's a good fit for companies with deep pockets.

It's powerful and amazing, but the costs can add up.

I'd give it an eight out of ten. It's super powerful and solves real problems with global distribution. I hesitate to give it a perfect ten because it's still new, and good training resources are harder to find. Even the most recent books on Cosmos DB are several years old, which is ancient in IT terms.

I had to work hard to get a certification in it. 

We use Cosmos DB to store the concept of data and how it is entered by the user.

Cosmos database has helped improve search result quality, allowing more results. We implemented the ASR service to gather data from users. Cosmos database does an excellent job of searching through large amounts of data. The speed is impressive, and integrating our power-up database with Kafka was an improvement.

The most valuable features of the Cosmos DB include its ease of use and optimization and its seamless integration with code. We do not use the built-in vector database capability, but its interoperability with Azure AI services is noteworthy.

One area of improvement for Cosmos database is the auto-scaling of RUs during high loads. It would be beneficial if the database could automatically scale resources rather than requiring manual adjustments.

I have been using Cosmos DB for two years.

To scale workloads effectively with Cosmos database, we must manually increase the RUs. During the initial implementation phases, we encountered issues with scaling, but it appears to have been resolved.

We replaced our SQL database with Cosmos and Kafka, resulting in an improvement in operational performance.

The initial setup was straightforward and did not take much time.

I rate Azure Cosmos DB eight out of 10. The system itself is effective for our current use cases.

The main use case for Microsoft Azure Cosmos DB is as a key-value store where we store all the user data that we have and perform lookups. We use it at a significant scale, with storage of unique data reaching 12 terabytes and handling up to 3 million requests per second.

The scalability of Microsoft Azure Cosmos DB has significantly aided us in managing workloads efficiently.

We were able to realize the benefits of Microsoft Azure Cosmos DB immediately after deployment, making it quite easy to get started.

The most valuable features of Microsoft Azure Cosmos DB include the TTL, the ability to scale up and down as needed, and geo-replication, which comes out of the box. We do not have to do anything for geo-replication. We just have to enable it.

The indexing policy is also very good, and the overall metrics and monitoring system are also quite good.

Microsoft Azure Cosmos DB is fairly easy to use.

Microsoft Azure Cosmos DB can be improved by providing more fine-grained control over certain aspects, such as connections and threads. There could be more control over how many connections are made. I am not sure if it is a knowledge gap issue. A regular connection with the Azure Cosmos DB team might help in addressing knowledge gaps. Being able to fine-tune these features would be helpful for us.

I have been using Microsoft Azure Cosmos DB for about six years.

Over the last two years, Microsoft Azure Cosmos DB has been very stable. It has very good latency and availability. Latency is good on the server side and the client side. We have had only one significant issue that affected our production system. Overall, stability has been excellent.

The scalability of Microsoft Azure Cosmos DB is one of its best attributes. We can scale very efficiently and adjust workloads as needed, which is more challenging with other systems.

We have contacted their support many times. The quality of customer and technical support has improved over the years. Initially, it used to take quite a while for issues to be resolved, but now the support is seamless and very efficient. We have not needed much support in the last couple of years due to the system's stability. It is pretty stable now. I would rate their support a nine out of ten.

Positive

I have used Redis briefly and Aerospike extensively before switching to Microsoft Azure Cosmos DB.

Both Microsoft Azure Cosmos DB and Aerospike have their own advantages. The biggest advantage of Microsoft Azure Cosmos DB is that it is very easy to get started with and it does not require too much effort. It takes just one click to deploy Microsoft Azure Cosmos DB and put it into multiple regions. It does not require too much maintenance, whereas Aerospike requires a lot of maintenance effort. It requires a dedicated team. In this aspect, Microsoft Azure Cosmos DB is very good. However, Aerospike provides control over a few things, which we do not have in Microsoft Azure Cosmos DB. If we want to run or use the maximum amount of resources, Aerospike helps a lot. Both have their advantages and disadvantages.

The initial setup was easy. It was not difficult.

It took us a quarter to be able to use it efficiently. It is fairly easy and straightforward.

We had set up our own autoscaler. There was a pipeline that ran on top of Azure Cosmos DB to see how many RUs were provisioned. It did require a little bit of maintenance because we built custom software on top of that, but that was it. Our autoscaler performed better than Azure Autoscaler. However, because of some billing benefits, we have started using Azure Autoscaler. The Microsoft team said that if we used Azure Autoscaler, they would give us a discount, so we started using that, but our autoscaler performed better.

Initially, the deployment required an entire team, but now, it can be managed by a smaller team of two to three engineers.

It has decreased our total cost of ownership by approximately 20% compared to other alternatives such as Redis.

Its pricing is higher compared to solutions like Aerospike. However, it is justified because of the out-of-the-box features that are provided. The availability and resiliency that we have make it worth the price.

To new users, I would advise first knowing their data. They should know whether it fits their solution, which Azure Cosmos API to use, and what scale they intend to run it.

I would rate Microsoft Azure Cosmos DB a nine out of ten.

I work for a retail company that uses Cosmos DB internally for access management. You have a graph with a hierarchal model that goes from owner to manager to assistant manager to employee, etc., and you provide access based on this hierarchy. Our workshop manager uses Cosmos DB to track requests for access and who needs to approve them. 

Employees who want to access specific resources will submit a request, and the application owners will approve it. Within the applications, there are often multiple levels of access. So the owner of those processes or files must authorize access. We have nearly 500 users. The security and access management teams mostly use Cosmos DB. 

The company is considering a switch, but that might take many years. Many others have switched and will continue to switch to other solutions. However, after you've invested a couple of years into it, it becomes more challenging because you need to rewrite many things. 

I don't think Cosmos DB has improved our organization. People are using it, but I'm not sure it's the best solution. For one, it's costly. Also, there are other issues with it. You cannot get all the records simultaneously. You can only get it in chunks of 1,500 maximum.

Cosmos DB is a graph database. I could see the advantages when we implemented it because it didn't have much competition. MongoDB was doing it, but it wasn't a popular solution for graphs, structures, and hierarchy. The only competitor was Neo4j.

I have been using Cosmos DB for nearly a year.

I rate Azure Cosmos DB eight out of 10 for stability if you allocate the necessary resource units. It is based on the concept of a resource unit. There are three settings: auto, manual, and another one I can't remember. You can manually set a limit on what goes to the resource unit during a specified time. or it will automatically send and continuously increase. 

This can create some instability. For example, if I limit my resources to 30,000 RUs, I expect to consume, but if the load is higher, it will fail and continue to fail. I will get an error that says, "Too many requests."

If you set it to "auto," you'll have to pay for it. You can adjust the limit, but it will not automatically do it. It requires someone who can think in terms of RUs, not the other databases we usually use. The person should always think in terms of resource units because you're paying for each resource unit. It isn't simply writing queries and pulling the details from the database. That is a steep learning curve. Many assume Cosmos DB is like any other NoSQL or graph DB. 

Cosmos DB is scalable, but there are some limitations on the amount of data you can hold in this partition. I think the maximum is 50 GB. That is a lot of data, so it is scalable, but there is a limit. It isn't infinite. Only 99 partitions are allowed with 50 GB each, then the maximum amount of data is under 5,000 GB.

However, it isn't simple because you need to define each record. You have to decide which partition the records should go to. Suppose I have 100 GB of similar records and want to put them in one partition. That isn't possible. 

I rate Azure support nine out of 10. They respond quickly and will help you manage costs. However, they mainly give you an overview of the issue, so they'll never have an in-depth idea of what you're doing. They aren't the owners of our product, so they don't know much about it, but they can ask you generally: What are you doing? Are you doing too many updates? How can we reduce the cost? 

They usually make common suggestions, but so few technical people understand Cosmos DB, and they will be costly. 

Positive

I have used multiple NoSQL databases. The most common is Neo4j, but people also use MongoDB, which is a little easier. You have optimization and all those features there.

I rate Cosmos DB nine out of 10 for ease of setup. The setup is easy, but backing Cosmos DB takes a little more work. It isn't difficult, but you have to raise a request to Azure support. It isn't in your control. The documentation is good enough that most application developers can handle it by following the steps in the documents.

We did it in-house. Two developers should be more than enough. One person could do it alone, but it's always good to have an extra person to verify that your actions are correct. After deployment, it doesn't require any maintenance. When you want to make a copy, you submit a request to the support team and within 24 hours. 

We haven't seen a return. You could benefit from this, but few engineers know how to use it correctly, so that's a problem. It depends on the company. I believe only large organizations can afford it. 

You also should be ready to invest in developers because it has a considerable learning curve. In other databases, you have something called "data cutover." You can change the whole concept of your data to make it more efficient. That is not possible in Cosmos DB. It's too big and will take years to change, whereas that might take you only two or three days in other databases.

For example, let's say you are paying a hypothetical amount for a mistake you made. We'll say it's $1,000. After a couple of years, you realize that you will only need to pay $200 after fixing that mistake, but it will require too many changes in multiple places to fix that error. You might need to discard your old solutions entirely, and it takes years to rewrite everything. Cosmos DB isn't going to reduce the number of people. Conversely, it's going to increase problems and create more confusion.

I rate Cosmos DB one out of 10 for affordability. It was expensive. We pay almost $1,000 daily to use it. It doesn't work traditionally — it works on resource units — so it's costly. It's a graph DB, which has advantages and disadvantages. Neo4j and MongoDB do the same thing, so it depends on your environment and costs.

There are also issues with how you design it. You cannot create the traditional way like you would in other databases or graph databases. Typically, you would pay a fixed subscription yearly. With Cosmos DB, you pay monthly based on the source unit. That's what is expensive. 

It's harder to find designers and developers based on that. Many solution architects will set something up using the traditional way of thinking. Once you start using it expensively, it's challenging to change that. You end up with millions of records, so it's impossible to change all of them. 

We are considering changing from Cosmos DB to MongoDB.

I rate Azure Cosmos DB six out of 10. I wouldn't recommend it. I suggest using other products like Neo4j and MongoDB. If you must use it, you should hire an expert who understands how to design the tables, indexing, and partition keys. The setup is effortless, but how will you write the code? It should be predetermined.

This is an event-driven solution. Most oil and gas companies have folder source systems, where they cannot scale, but they still want to provide real-time data to their end consumers for various analytical use cases and AI/ML processing; this is where we input raw data into the Azure environment of this solution. Then, eventually, we built the API on top of Microsoft Azure Cosmos DB because it's highly scalable. The solution is a little bit expensive, but the businesses are ready to accept it. 

In terms of performance versus scalability of this solution, you don't need to worry as long as you have your initial numbers in place. This product works by using performance currency, which is the number of request units per second. Once the data is ingested, based on that, we can know how many users are going to access across the world in every day, hour, or minute. Once you have the ingestion versus consumer pattern identified, you can use this product to input all those numbers, like the volume of data for migration. 

The built-in integration of the solution is tight. It can be used in conjunction with Synapse, Microsoft has also created a Synapse link. In this solution, the OLTP workload will never affect the OLAP workload. Therefore, the solution does data replication asynchronously without affecting the OLTP source system. No specific pipeline is thus required, which is not easily found in other services. 

In the server, there are two ways in which you can provision a call, one is serverless, which has a pay-as-you-go model, and another is a dedicated provision throughout. So, irrespective of what you allocate and whether you use it or not, in this solution, the charges are accounted for the request unit per second. This is a big drawback of the solution.

This is an expensive solution and if you get the initial calculation wrong involving how much you are going to ingest, how many people are going to query and more, then you are going to receive a very large bill at the end of every month.

Additionally, on the serverless option, there is a limitation regarding the amount of data you can ingest; this doesn't allow you to upgrade beyond a point, and the limit cannot be utilized for many use cases. On the execution side, whatever you create as a container, that container cannot be used as a destination when using serverless mode. This is another key limitation of the solution. 

I have been using the solution for one and a half years. 

I had minor issues while using the solution, but they were actively solved, and eventually, a justification was also given. Ninety-five percent of the time I used this solution, there were no issues. Microsoft's service in the cloud market is still growing and so there are some feature limitations. 

The solution is highly scalable. We use the solution in our enterprise both internally and externally, including integration for clients. We created our solution end-to-end by considering different audiences, people who can directly onboard Azure but might not need Cosmos DB.

There are vendors and individuals who cannot directly consume data on the Azure environment and will have a dependency on data. However, we cannot expose the source data for its performance issues and limited scalability, so we deliver this data by using Microsoft Azure Cosmos DB. The parent company Reliance has multiple subsidiaries like Ajio, manufacturing supply chain, Oil and Gas, and more; we used to use the same API for all subsidiaries, which was built on Cosmos. 

Technical support was good. I would rate the customer support an eight out of ten. They were fast and responsive, but the support team runs from different locations within or outside India, so whoever is working during the particular shift will take care of the case initially and then some other individual will take over.

So, my team had to re-explain the same thing over a call or meeting. But it was only a few times, they were able to get all the information based on the previous conversations most of the time. 

Positive

Deployment of the solution was very easy. Once the initial numbers are in place based on request units, only the instance creation was a time-consuming process. The time was consumed due to the dependency on other teams like DevOps, who are responsible for provisioning. So, it was a one-time process, but migrating and running the same workload between different environments was not much of a hassle. 

It took less than a week to configure and install this solution. To complete the setup, it took five to six professionals from our team. One key solution architect, two people from the DevOps team, and two solution architects from Microsoft were needed for the deployment of this product.

Maintenance of the solution is very easy because the solution follows a Platform-as-a-Service type of model. There is actually no need for any downtime or a patch upgrade because it is taken care of by Microsoft. I never have to worry about downtime for this solution. They perfectly deliver on the key characteristics of the product. 

Our business need was to deliver or provide the source data without any latency issues, in less than five or ten minutes latency, to be precise. We had to provide the data to the end consumer without overwhelming our source. We got the business confidence in the initial three months of using Microsoft Azure Cosmos DB. 

The solution is a bit on the expensive side. 

We tried to compare this solution with MongoDB, which is open-source. But we choose this solution because Microsoft is the first implementation partner for us. 

I would rate the solution an eight out of ten. My advice to other people will be first to identify the purpose of availing the solution. There is also a product called Azure Data Explorer, which is a more extensive service used for similar use cases.

Also, in terms of Cosmos, the user should be clear about whether they will be able to use the serverless deployment model or whether they need the dedicated provisioned throughput Model. They should also first use the price calculator by inputting the numbers to decide if they need it. I would also advise you to get in touch with a Microsoft Specialist and walk through all the doubts. 

The solution has a very good service, but the user should be clear about how to start using the product. For the initial three months, we did a lot of trials to get the components and RUs right and check how the calculation is happening. However, after the trials, we were very clear about how we wanted to move forward with the solution to get the maximum ROI. 

In our setup, we rely on Azure Cosmos DB primarily for cloud-native applications that demand global scalability. We use it for connecting web apps and implementing search functionalities.

Cosmos DB's low-latency data access has greatly improved our application performance. It is a game-changer, allowing us to move workloads from on-premises to the cloud, thanks to the reduced latency, and freeing us from the constraints of on-premises environments.

The most valuable features for our organization with Azure Cosmos DB are multi-master capability for applications, automatic failover ensuring high availability, scalability, support for multiple data models, and low-latency access. Additionally, the seamless integration with microservices running in containers adds another layer of efficiency to our operations.

In terms of improvement, slight enhancements in integration interfaces, expanded dashboard functionalities, and broader use-case support would be beneficial.

I would rate the scalability of Cosmos DB as a nine out of ten.

The technical support is quite good.

It is a relatively affordable solution.

For those considering Cosmos DB, my advice is to embrace its versatility. Cosmos DB can handle various data models like documents, wide columns, and graphs seamlessly. You can consolidate your needs into one database, Cosmos, eliminating the need for multiple databases. It simplifies management and offers a comprehensive solution for a wide range of use cases.

Overall, I would rate Microsoft Azure Cosmos DB as an eight out of ten.

I worked on an entity data client project that handled multiple users from digital products, like a Stream player similar to Netflix. It is called Play Plus, with a user base of seven million and a website that attracts eighty-two million unique users per month. We store the data from these users in Cosmos DB to ensure a fast connection between the user profile and the advertising system.

I believe the best features of Cosmos DB are its global distribution capabilities. It supports multi-region replication with low latency in read and write operations and automatically adjusts throughput dynamically based on workloads. It ensures low latencies, offers consistency levels, and provides a serverless mode. Also, for this scenario, the solution was fast due to its serverless nature, allowing it to handle eighty-two million user profiles with low latency and swift connectivity with the advertising system.

I think Cosmos DB enhanced the benefits of NoSQL databases, offering query flexibility, partition management, and backup and restore options. These aspects are crucial, and perhaps improving the complexity of partition management would be a beneficial area for Microsoft to focus on.

I have been using Cosmos DB since 2023 when I executed a project for clients.

I do not experience problems with Cosmos DB, nor does the team have any issues with it.

I have used both Cosmos and AWS DynamoDB. Compared to Cassandra and Cosmos DB, I think Cosmos is a superior solution because it is serverless. There is no need to worry about installation, updates, or security, as Microsoft handles all this. Our focus is solely on development.

The setup was easy, very easy.

By using Cosmos DB, I estimate at least a twenty percent saving because it allows us to develop rapidly.

I truly recommend Cosmos DB because it is a serverless product. Our responsibility is just to develop and adhere to Microsoft's instructions to achieve optimal performance from Cosmos DB. It is an excellent tool. The product rating is nine out of ten.