Microsoft Azure Cosmos DB Reviews

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.

We use it as our main database for our network access control software, and we use it to store all of the information we need to authenticate different devices and users to the networks of our customers. It maintains all of the necessary data for our SaaS product.

It is pretty easy to maintain and to optimize. The main thing that we have to deal with is the RUs. Probably the number one topic of Azure Cosmos DB in the world is how to make sure you have the right RUs set in place for each of your different collections, but the tools that are available in the Azure portal make it very easy for us to check how the performance is going. We can check if we need to adjust anything within the system to ensure that we have the right scale and the right split of our different indexes so that we are getting the most throughput for the data we need. In general, it is very easy for us to maintain. We just need to use the Azure tools to let us know when we need to pay attention to the different throughput variables that are important in the general maintenance of the product.

We use Azure AI Search for all of our search needs. The integration that Azure AI Search has with Azure Cosmos DB is very good. We heavily utilize the integration with Azure AI Search for a lot of the features on our website. The search works very well. I do not know if we have what one would consider large amounts of data because each one of our customers is searching through just their own set of data. On average, each customer has data in the range of only gigabytes, so the search does very well, but I do not know if you would consider what we have as very large amounts of datasets. We are dealing in the few gigabytes range rather than anything huge like terabytes.

The benefits of its global availability and the response time were noticed right away because we immediately had customers who were globally distributed, so the latency was noticed right away. We got a good performance that way. We did not notice any of the other benefits until we got a lot more customers and had a lot bigger scale. As our scale has increased dramatically over the past couple of years, we have noticed that we have not had to do much with Azure Cosmos DB. It just takes all of our additional data and additional queries and all of the additional throughput that we are throwing at it. It does not need very much in terms of maintenance and performance tuning because it handles everything we need pretty much out of the box, so it is a low-maintenance solution for us where we just check on things every once in a while as a best practice. In terms of scalability, we have doubled, tripled, and quadrupled our customer size or number of customers, and we have not had to do very much with it architecturally just because it has been able to handle that scale.

For onboarding, the documentation is very good. As soon as I joined the company, I read a lot of the Azure Cosmos DB documentation so that I understood it. It is well documented, and there are support forums and Microsoft experts. We have a Microsoft Solution Architect dedicated to us, and we have been able to ask him questions. The community surrounding it and Microsoft's ability to answer all of our questions, my questions specifically, have been really good. The documentation is great. I have been able to find all of my answers during my tenure. Everything is generally answered in the documentation, and for what is not, Microsoft has been very quickly able to get to us through our Solution Architect.

Within the first week, I was already executing queries against the database and monitoring its performance. Within a week, I had a basic understanding of how to interact with the database and understand different performance metrics and structures within the database. It is pretty easy to learn if you are familiar with other databases. Because I was familiar with other document stores and SQL-based databases, there was not a lot to understand. There are some differences in the SQL language that you are allowed to use with Azure Cosmos DB, so there was a little bit of a learning curve there, but the documentation was really specific. It is not a sharp learning curve if you are familiar with any other database systems. If you are familiar with SQL Server, MongoDB, MySQL, or Postgres, a lot of the concepts are exactly the same.

We chose Azure Cosmos DB initially because of the type of data that we needed to store. We have a schema that is very nondeterministic and flexible. It is always changing based on whatever data we need to acquire from different devices, so we needed a document store with a flexible schema. 

In addition to that, our customers are globally located, so we needed a database store that could be globally accessed and had minimal latency, good throughput performance, good query performance, as well as scalability. All of the things that you look for in a good piece of software about performance, scalability, high availability, and disaster recovery are available in Azure Cosmos DB. Because of that and because it is a flexible document storage, we went with Azure Cosmos DB.

The one thing that I have been working on with Microsoft about this is the ability to easily split partitions and have it do high-performance cross-partition queries. That is the only place where either our data size or our throughput has grown beyond one partition, so being able to do cross-partition queries efficiently would be my number one request.

The request unit architecture that they have in place is understandable but could be better. What you get out of some solutions like SQL Server or MySQL is a lot more understandable. The request unit architecture of Azure Cosmos DB is not as easy as pure SQL solutions. They could do better in making the RUs more understandable and more flexible because changing your partition keys and your indexes is a larger batch of work than it necessarily needs to be.

The company I have been working for has been using it for more than five years, but I have been with the company for just over two years. I have been working with Azure Cosmos DB during my entire two-plus years at the company.

We have had only one incident where Azure Cosmos DB went down. It was about two years ago in the East US. They had an incident where the update they made caused some downtime for us. I forgot what the duration was, but luckily, we had global replication for our main Azure Cosmos DB setup, so while the East US was down, the West EU region picked up. The majority of our operations continued without issue because of our use of the global replication option available within Azure Cosmos DB.

Latency and availability are great. You can, of course, write code that does bad things for it, and we have had to fix our own code sometimes. Whenever we have written our code properly, latency and availability have been great.

The scale has been wonderful. Our ability to add request units as we have needed them has been easy. We do not have to do much other than tell the system we want more, and then it automatically scales for us, so we do well there. 

The only limitation is around the partitions. Each physical partition maxes out at 10,000 request units as they have documented. We have had to deal with that while designing our data structures to make sure that we take into account that physical partition limitation.

The quality is top-notch. We have been able to talk directly to some of the Azure Cosmos DB experts at Microsoft. We have been able to get extremely detailed answers with very specific recommendations for all of our different questions.

Their speed has been definitely acceptable. Within a day or two, I get at least an acknowledgment of my question. We have not had any high-severity questions to be answered right away. Most of our questions are during the design phase where we just need to know specific recommendations based on our needs, so there has been no real-time pressure. Answering or acknowledging our question within a day or two has been an acceptable time frame. We generally get our answers within a week, which has also been acceptable for us. We have never needed a super fast answer, but I am also clear about that in my communication with them. I tell them that it is not an emergency, and we are just in the design phase and need these questions answered.

I would rate their support a ten out of ten. There is nothing that I would ask for more from the support. They are responsive. They give accurate answers, and they are easy to deal with. That is all that you want from a support experience.

Positive

Historically, in my career, I have used SQL Server, ClickHouse, Postgres, and MySQL. ClickHouse is probably the closest equivalent, but we had to maintain everything in-house, so it was a lot more intensive to maintain. SQL Server was nice, but it does not have the document flexibility, or it did not have that at the point in time we were thinking of using Azure Cosmos DB. So, I am very familiar with all the SQL-based servers historically. They just did not have the necessary document flexibility that we were looking for when we selected Azure Cosmos DB.

It was already in place when I joined.

It does require some maintenance as we grow. Each one of the collections scales up by request units. We use the autoscale feature, but it has a bounded range, so as we grow, we watch the RU maximum, and we adjust the RU maximum of our different containers as we scale up. The maintenance is minimal. We change the RU scale maybe once per quarter. Otherwise, everything goes fairly without maintenance.

I do not know if it has helped us decrease the total cost of ownership, but certainly, it has helped in our DevOps maintenance. Our DevOps people spend a lot less time worrying about or dealing with Azure Cosmos DB. Because of that, we do not spend a lot of person-hours on Azure Cosmos DB. However, Azure Cosmos DB does come at a premium price in terms of being able to do all of its features. It has an appropriately associated cost with it. Because of that, we have not done any formal calculations to see if it saves us more than some of the other solutions such as SQL Server or Postgres. We have not done a cost comparison. What we do know is that it satisfies all of our needs. We do not spend a lot of time thinking about it. As we add features and datasets to it, we do not have to do a lot of performance testing, so we are just able to add things to it. It just works, and we do not have to spend a lot of development time, QA time, or DevOps time worrying about whether it is going to be able to satisfy what we need it to do.

As opposed to running our own VMs or our own databases, it would have reduced our overhead costs. All we have to do is go into the Azure portal, click a couple of buttons, and type a couple of numbers, and then it just happens without any other effort. It takes us seconds to minutes to change things, whereas other solutions might take days or hours to process. From that perspective, there is certainly a reduction because it only takes us a few seconds to scale our Azure Cosmos DB without any other effort. However, you pay for that with the actual price of Azure Cosmos DB itself. That is somewhat built into the price where Microsoft takes on that maintenance cost, but you pay for it.

The pricing and licensing model was initially difficult to understand, but as soon as I learned what was going on and how it was priced, it was pretty easy. What is more difficult is to understand how your system is going to behave specifically with the specific partitioning and querying that you are doing. Some of it is reactive because you cannot always predict what your customers are going to use in your product and in what specific way. So, while we have understood the pricing model, what we have not understood is which parts of our system would end up being the most expensive, costing us the most, or needing to scale the most. It is not necessarily an issue with Azure Cosmos DB itself. It is about understanding your individual software or our individual software when it is running on top of Azure Cosmos DB. It is about understanding what the behavior is going to be.

I would advise taking advantage of all of the features that are available. Especially if you are a globally distributed business, make sure that you have all of the high availability and backup options enabled so that you are not surprised in case of a problem.

Like almost all of the recommendations that you see in different Microsoft videos, make sure that your partition keys are set up properly from a RU perspective so that you know that you will be able to scale your individual containers effectively without running into the limitation of 20-gigabyte physical partition size or 10,000 RU physical partition throughput. Be aware that those exist and design your partition keys for the future so that you will not be limited when your system starts to get heavily utilized in the future.

I would rate Azure Cosmos DB an eight out of ten. There are some improvements that I would like to see around the physical partitions.

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 use Microsoft Azure Cosmos DB in our loyalty platform, which is based on our proprietary technology, Synapse LX. In loyalty, we need to enroll, score, and deliver rewards communications in near real-time. There are significant volume spikes in those activities, so our use case is to support the writing of information into our database. Cosmos DB is a no-SQL database that allows us to scale quickly and handle large volume spikes. It allows us to auto or manually scale in many different ways. It gives us much flexibility to handle that requirement and ensure we deliver the right customer experiences.

Cosmos DB is not difficult to use, but like anything, it requires careful planning and consideration of use cases. This is especially important when planning to implement it. From an optimization perspective, Microsoft has made significant efforts in the past 12 to 18 months to facilitate changes after initial implementation and optimize cost.

Cosmos DB provides excellent search result quality. Since implementing it, we have not encountered any issues with our searches.

After deploying Cosmos DB, we initially experienced some performance gains, followed by additional benefits that required a learning curve regarding tuning and configuration. As our understanding deepened, we were able to optimize it further.

In the last three months, Cosmos DB has helped reduce our total cost of ownership. Microsoft recently implemented a feature that allows us to achieve savings of up to 50 percent.

The most valuable aspect of Cosmos DB is its performance. It serves as the foundation for OpenAI's infrastructure, providing us with similar functionality. This not only prepares us for AI use cases but also efficiently supports our loyalty use cases. We can share information with our customers and deliver experiences without concern about performance.

For our Disaster Recovery plan, we currently use geo-replication. We'd like to avoid full DR replication if possible, as this would result in significant cost savings.

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

We have not had any stability issues with Cosmos DB.

Cosmos DB's scalability is excellent, which is the whole reason to use it for scalability and performance.

The dynamic scaling helps decrease our overhead costs.

The initial deployment was straightforward and consisted of two to three people.

Cosmos DB's pricing structure has significantly improved in recent months, both in terms of its pricing model and how charges are calculated. This has led to substantial cost savings for both us and our customers.

I rate Microsoft Azure Cosmos DB seven out of ten because of the disaster recovery requirements.

Cosmos DB presents a steep learning curve. I would rate it a five out of ten. The challenge lies not so much in understanding its concepts as in utilizing them effectively and efficiently.

It took us 12 to 18 months of focused attention to fully onboard our team. At that point, we began to understand. However, it wasn't until we went live and observed actual user activity that we truly grasped the whole picture. Testing is one thing, but experiencing real-world interactions provides invaluable insights and a deeper understanding.

Cosmos DB requires minimal maintenance, but monitoring its performance and optimizing it as needed is crucial.

Potential users should plan accordingly, as Cosmos DB is a NoSQL database that uses similar design principles. Consider the design and apply those principles beforehand to optimize performance from the start. Understanding your read-and-write ratio is crucial due to cost implications, so ensure you understand the balance between reading and writing to the database. All these factors matter as they can impact your costs, so consider them carefully. 

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.

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. 

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.

We use Cosmos DB as our entire storage database solution for our application. We don't use any other relational database. We have a file that we use for configuration, but we use Cosmos for user data.

We have about 100,000 users a week who visit our website. We have plans to increase usage to four times what we're using now.

The biggest benefit it offers is scalability. It's easier to work with concurrency and updating data. We don't have to worry about locking cables or the speed of reads or query searches because we've structured our data around a key value. Everything is super fast, and it basically removes any bottleneck related to databases in our application, and we just use reference keys. One document will reference the key of another document that we need, so we don't have to rely on searching.

Partitioning is helpful because we use it heavily. Partitions are really nice because they help with the collection of data. Not only is it fast to recall the data, but when you partition it, you can pull the partition and then query the exact document from that partition. It helps with data recall.

There's another feature that we just started implementing, which is partial updates of documents. It doesn't require the entire object to update, but updating documents across applications becomes difficult because you have to pull the entire document, which means you have to support the entire model to update it. So, that application has to know about every single parameter that may or may not have been added because if it reads and writes the document again, you'll lose data elements.

An improvement would be a more robust functionality around updating elements on a document, or some type of procedural updates that don't require pulling the entire document. Otherwise, you have to keep all of your apps up to date with the models, and that can be cumbersome and lead to errors. Usually, you don't always remember, and then it leads to some type of bug, but you won't realize why. You'll lose some value because you don't realize that you have some application that doesn't run often. You forget that it writes to that same document and you didn't update the model.

It would be nice to have some type of functionality for less common updating applications and to not always have to worry about keeping that model up to date.

There's some integration with Entity Framework and it's nice, but it's not robust and it would be good to have something like that when it comes to pulling data.

Occasionally, you have to query the database for values because we save our appointments and we don't have an index on appointments. We don't have a manual lookup for appointments, so we don't save it in another file. We have to run a query to get appointments that occur on a specific day and the downside of that is you have to use strings just to hardcode the string values. It would be nice to more easily integrate with a tool like Entity Framework, and I know that they do, but it's not an easy process. It would be nice to have an easier way without relying on text to query the database.

I have used Cosmos DB for a year and a half.

There have been some configuration issues, but we haven't hit any thresholds or roadblocks when it comes to throughput. That was one of the reasons that we leaned toward it and not a relational database, especially at scale. We haven't run into any issues when it comes to that.

We look at community answers because we can usually get answers faster than messaging support directly. We don't usually resort to a customer service type of support unless it's a fundamental issue. 

When we had an outage in the middle of the night, the turnaround time was within a few hours.

Previously, I used Couchbase. I've also used Neptune, which is a different type of database. I've also used SQL.

We chose Cosmo DB because it's more tightly integrated. One of the reasons we chose this version of a non-relational database was because of the speed of development. We also chose Cosmos DB over other types of NoSQL databases because it's so tightly integrated with Azure, it's easily managed through deployment templates, and it's very easy to scale. If you're using Azure already, it's a very easy tool to pick up and integrate into your applications.

Cosmos is pretty straightforward. There is more complexity, so you just have to be mindful. We had a small issue with making sure that the disaster recovery settings were set up correctly. We found out that there was some type of outage in the middle of the night, but we noticed that the failover didn't run properly. It was because of some configuration that should have been caught earlier, and it wasn't obvious that we got it wrong.

There are some infrastructure teams that manage some underlying resources that are related to Cosmos and some of the configurations, but for our specific implementation, we have three developers at most. We usually only need two people for maintaining and managing the solution.

We deployed the solution in the cloud, but we configured everything in-house.

We have seen ROI. There's no active management when it comes to that. When I've worked on relational databases, there's a lot that goes on, like indexing, upgrades, and store procedures. I've managed relational databases for years while working on an application and worked with people who managed them. Cosmos is nearly maintenance-free and very easy to use.

The pricing is really good. I would rate the cost as 9 out of 10. There may be some more complicated use cases that are more expensive. When we've budgeted for our resources, it's one of the more expensive ones, but it's still not very expensive per month.

I would rate this solution as 8 out of 10. 

When it comes to ease of use, spinning up and working at scale, our specific use case, and the scalability that it offers, the solution is definitely very good.

My advice is to use containers as single objects and create manual indexing to improve efficiency.

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.