Microsoft Azure Cosmos DB Reviews

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.

We mainly use it for products that are based on graph concepts. We are using it for mobile applications and real-time analytics.

We have scaled it from 400 users to more than 1000 clients. We were able to scale efficiently during COVID-19.

The graphical representation of data is the most valuable feature of the solution. We did not face any glitches.

The support tickets are not cheap.

I have been using the solution since 2017.

I rate the tool’s stability an eight out of ten.

We had around 300,000 users. They were distributed globally. I rate the tool’s scalability a nine out of ten.

The support team is not competent. We end up with the wrong agents sometimes. Sometimes, we must buy support tickets. It is not a good idea to have tickets that cost a lot.

Negative

It is a cloud-only solution.

We have also used MongoDB and SQL Server.

We had some challenges at the beginning because our team did not know how to optimize the tool. They made some expensive applications. However, we were able to cut it down by 95%. Overall, I rate the product an eight out of ten.

Microsoft Azure Cosmos DB has helped improve the search result quality of our customers' organization. The customer gave us the feedback that they are able to easily find the data they are looking for. It is very quick. That is the best feedback. They have a large amount of data, and they can find it quickly.

Our customer is very satisfied with it. Our branch does not use it yet. We used it for a customer, and the customer is very satisfied so far.

There are no particular factors that need improvement. There is a little bit of a learning curve with scaling workloads, but it works smoothly.

I am still learning it and have only used it for one application so far. 

No issues have been reported regarding the stability. It is very fast.

There was a little bit of a learning curve with scaling workloads, but overall, it went smoothly.

I have not had any challenges. However, the configuration done in a session at Microsoft Ignite looked complicated. It would be a bit challenging to do the same configuration. 

There is a little bit of a learning curve. The onboarding process for the team took about two weeks.

The only feedback the customer gave us was that it was way cheaper than they expected. 

The customer had a high budget, but it turned out to be a little bit cheaper than what they expected. I am not sure how much they have spent so far, but they are satisfied with the pricing.

I would recommend this product. I would like my organization to develop and explore it further. I would rate Microsoft Azure Cosmos DB an eight out of ten.

Cosmos DB has multiple use cases. For instance, we recently developed a custom application for a customer in India. We used Cosmos DB to store data fetched from the initial front end to reduce access times to the application, which is significant for improving user experience. 

For example, when creating a virtual machine through our custom portal, it is essential to check whether a VM with the same name exists in the same or a different subscription. 

Additionally, we needed to enforce naming conventions and limitations on the number of VMs that can be created within the same network. These conditional parameters were managed using Cosmos DB, allowing the initial provisioning process to validate data and configurations instantly. 

This enables us to inform the user right away if there is a duplication or if the creation adheres to predefined rules, offering suggestions based on the UI. This demonstrates the real-time application and benefits of Cosmos DB.

We use Cosmos DB for its key-value storage capabilities. For structured data, we always use SQL Database.

The low-latency data access provided by Cosmos DB improved application performance.

Application performance improvement depends on what kind of optimization you're looking for. Do you want to improve latency or efficiency? Performance tuning depends on that specific goal.

Here's an example: A customer with an application running in an internal system noticed their outbound data flow and charges increasing every month. They were exporting a lot of data for users in Excel format, which was heavy.

I suggested they export the data in CSV format instead. It's lightweight and users can still open it in Excel. This optimizes data usage and costs without compromising user experience.

Cosmos DB now supports unstructured data. It's a key-value store, so we can send data without worrying about strict structure, data types, and so on. Since it's unstructured, it's lighter than a structured database.

We use Cosmos DB for its key-value storage capabilities, while SQL Database is used for structured data.

There is room for improvement in terms of stability. 

I have been using it for a year. 

In my experience, Cosmos DB is definitely stable. But, for any service or application, I wouldn't give it a perfect score. There's always room for improvement. A perfect score would mean no room for improvement. So, I always consider some buffer for improvement.

I would rate the stability a seven out of ten.

Since it's a managed service, Azure backend handles scalability. From a user's perspective, we don't need to worry about scalability.

Right now I'm dedicated to customers of one of India's largest certificate authorities, Reliance Jio. They have a lot of customers and two dedicated Azure data centers in India. I focus on those two data centers, and I see at least 10 to 15 customers heavily using Cosmos DB there.

From the user's perspective, it's a managed database service, so all scalability is managed in the backend. Users shouldn't worry about scalability itself, but they might need to consider if paid region support is needed or if other regions are available. Otherwise, scalability shouldn't be a concern for them.

But if you're configuring Cosmos DB in a non-Azure solution, you'd have to manage scalability yourself. In that case, you'd have to be more conscious about it.

We have dedicated technical support in India for each Azure service, including Cosmos DB. Since I provide the framework, design, and initial implementation, I'm involved in most calls to ensure everything is deployed as designed. 

But for any issues or troubleshooting, there's dedicated support that gets involved and fixes them. I also stay engaged with the product team.

The product team is very proactive.

Positive

I predominantly work on Microsoft SQL Database, among others. Sometimes, we use Cosmos DB for specific adaptations or APIs within Azure.

We've also assisted some customers in migrating from MongoDB to Cosmos DB.

If you're familiar with it, it's not complex at all. But for someone new, it can be a little tricky.

Cosmos DB itself is a cloud-based solution. However, I'm currently working primarily with a hybrid solution: Azure Stack HCI with software-defined networking for the environment.

We don't directly deploy Cosmos DB itself; it's a service within Azure. We use our DevOps pipeline to deploy the entire environment, which includes the application, database, environment (including the virtual network), and any connected service endpoints. 

Everything gets incorporated into the provisioning source or the DevOps pipeline and then deployed from there. It's a pretty streamlined process for us.

If the cost is affordable and you're looking for a managed service for unstructured data, I would definitely recommend using Cosmos DB from Azure. It also has seamless migration options from MongoDB, MySQL, and others. 

So, a managed service is the best way to go if the cost is affordable.

Overall, I would rate the solution a seven out of ten. 

We are streaming some data from Azure Stream Analytics, which will be stored in Microsoft Azure Cosmos DB. Our application will be taken from Microsoft Azure Cosmos DB.

The solution's most valuable feature is its global distribution. We work globally and currently have Azure operating in fire regions. From a global distribution perspective, Microsoft Azure Cosmos DB is good and easy to handle. Since Microsoft handles the solution's main operation, we don't have many headaches regarding its operation.

The solution’s pricing could be improved.

I have been using Microsoft Azure Cosmos DB for more than 2 years.

Microsoft Azure Cosmos DB is a stable solution.

I got instant technical support from Microsoft during an outage issue.

Positive

We use Terraform scripts for the initial setup of the solution, which doesn't take much time.

We implemented the solution through an in-house team. We select which region to host Microsoft Azure Cosmos DB based on the resource group. We use Terraform scripts in the deployment process. We create a database and a document inside the database.

The solution is pretty good in terms of support, but we have some pricing issues with it. We are currently evaluating MongoDB and Apache Cassandra. Apart from the pricing, we didn't face any issues with the solution. We once faced an outage issue with Microsoft Azure Cosmos DB because some back-end updates from Microsoft changed the settings.

Microsoft Azure Cosmos DB is a cloud-based solution. Based on our experience, the solution is pretty good because we operate in multiple regions. There will be a lot of machines sending IoT data, dashboards, and alarm messages. Customers need to be updated simultaneously, which should not take much time. The solution's processing is pretty good.

I would recommend the solution to other users. The solution's usage is pretty good, but users should be careful about the IO threshold value, which is a little bit high.

Overall, I rate the solution eight and a half out of ten.

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. 

I used to work for a bank in Turkey and used Microsoft Azure Cosmos DB in the bank for reporting. We used the solution for customer relationship management (CRM) and cost management.

Microsoft Azure Cosmos DB is easy to use and implement for application programmers.

The integration of the on-premise solution with the cloud can be difficult sometimes.

I have been using Microsoft Azure Cosmos DB for four years.

Microsoft Azure Cosmos DB is a stable solution if you use it on the Azure cloud.

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

Microsoft Azure Cosmos DB is a scalable solution. Currently, 10,000 users are using the solution. They use the dashboard application, but the dashboard application calls the data from Microsoft Azure Cosmos DB.

The solution's initial setup is straightforward if you use it on the Azure cloud.

We use a Microsoft subject matter expert (SME) to integrate Microsoft Azure Cosmos DB with the cloud or banking application. Microsoft Azure Cosmos DB can be deployed in one day. The solution's implementation is very easy in the Azure portal, but the most time-consuming step is to define the old data model in Cosmos DB. The security and the integration between Azure and on-prem are also time-consuming.

Microsoft Azure Cosmos DB is moderately priced, where it is neither expensive nor cheap.

The solution's licensing is usage-based. You will have an enterprise agreement if you use the solution in a cloud environment. The enterprise agreement is complex, where it is usage-based in addition to a base price. They decrease the solution's cost for an enterprise agreement, calculate the usage, and charge monthly bills.

Microsoft Azure Cosmos DB was deployed on the cloud in our organization. Only two or three people are enough to deploy and maintain the solution. Microsoft Azure Cosmos DB is the best solution for customers needing high-quality technical support.

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

Our current project primarily relies on the file system to handle incoming source tests. Within this setup, we capture both metadata and result data from these tests. We extract metadata information from these files and store it in Azure Cosmos DB and we have several software services in place to facilitate this process.

It is one of the simpler databases to work with in terms of code management, tracking, and debugging due to its straightforward data storage and retrieval mechanisms.

There is room for improvement in their customer support services.

In one of our recent projects, we stored metadata information and log data within Cosmos DB.

It offers good stability capabilities.

It offers impressive scalability, both in terms of throughput and storage. Its ability to scale dynamically allows us to align the database resources with the specific demands of our applications. Given its scalability and performance capabilities, we highly recommend it for use in large enterprises and organizations.

There were instances where their customer support services were slow. As previously mentioned, when it came to setting up Azure Cosmos DB, not everyone was proficient in cost considerations, and our team lacked extensive prior experience. Our main support was provided by Microsoft's documentation and we were able to successfully navigate these challenges. I would rate it eight out of ten.

Positive

The initial setup presented some challenges and required us to delve deeper into understanding the daily implications. Microsoft documentation proved to be a valuable resource in navigating this process.

The initial setup, planning, and configuration took approximately one to two weeks to complete. The timeline for implementing the solution varied based on the specific use case and the discussions held with the client. We conducted regular reviews, documented our progress, and established a static attack system. Due to some design-related confusion, the overall implementation process was extended to about one to two months. Still, Cosmos DB and related components were set up within one to two weeks.

Its pricing structure is quite flexible. It operates on a pay-as-you-go model, which means the cost is directly tied to the resources you consume and the throughput you require. Initially, our expenses were relatively low because we didn't store a significant amount of data, but as our storage needs increased over time, our expenses naturally grew in proportion to the resources and capacity we used.

Initially, we encountered some challenges in understanding it, as it wasn't as straightforward as managing an SQL Server database or setting up environments within Azure Data Factory and DevOps. This complexity is related to the fact that Cosmos DB offers a range of additional features and capabilities. Our initial difficulties could also be attributed to our team's limited prior experience with Cosmos DB. Considering these factors, I would rate our experience with it at an eight out of ten. Beyond these initial hurdles, we found it to be a valuable and capable solution.