Microsoft Azure Cosmos DB Reviews

We use Azure Cosmos DB as our data storage or database technology platform, and we use it as the backing storage of our metering and billing back office system.

We have an energy metering and billing solution, a SaaS billing solution, which is responsible for the whole back office for district heating and cooling suppliers. Our platform is responsible for the ingestion of time series data, and at the end of the processes, we generate invoices, which are sent out to customers. On top of that, we provide a consumer portal where consumers can view their energy usage and consult their bills. 

They are two separate products, and both are using Azure Cosmos DB. The B2C or the consumer portal is using Azure Cosmos DB serverless because of its very spiky nature. It is very unpredictable how many users will be using the B2C portal, and the back office application is using Azure Cosmos DB with provisioned throughput with auto-scale configured, which makes it very scalable and still cost-effective.

The uptime is very good. Over the six years that we have been working with Azure Cosmos DB, it has not let us down even once. We never had any downtime with the service. There is a very high SLA. We do not use the multi-region scale and multi-region deploys, but what we do use is the availability zone setup on Azure Cosmos DB, so we have West Europe and North Europe paired, which makes it very cost-effective to have a failover to a different data center in the same availability zone on Azure. That is the most important part.

Its performance is outstanding. It is very fast. Its evolution and the approachability of the product team have also been good. I have been working with their product team for a while. They have sent over a lot of questions and we have had a lot of interviews, calls, conversations, and discussions on how to best approach certain architectural decisions. We can also discuss and understand how to adapt new features to our infrastructure or architecture to use those features to the fullest. I appreciate it. They are very reachable.

With regards to optimization, it might sometimes be a black box. It is not like SQL where you have indexes, for example, and you have a query plan with indexes, so you can set up and tune to improve your query performance. In Azure Cosmos DB, by default, everything is indexed, which can be good, but it can also be bad because it can impact performance. It is difficult to understand which indexes you really need. You have the basic indexes, all fields being indexed, but then you have composite indexes, which are not created automatically. You need to create them manually. It is difficult to get insight into what type of composite indexes you need, so there is some work there. On the other hand, you can easily follow the resource usage. You can monitor whether your databases are nearing their full resource availability. You either need to scale up or adapt auto-scaling. That is useful with regard to usage. If you are used to NoSQL, you should be able to get up to speed with that pretty fast. We use Azure Cosmos DB for NoSQL. That is a specific provider. We do not use MongoDB, Cassandra, and so on. That means that the syntax to query is SQL. You use a sort of SQL syntax, so the step is really small to go from a different NoSQL provider to Azure Cosmos DB. Of course, if you go from a relational database to a NoSQL database, that is a different story.

We could see its benefits immediately after we deployed it. Immediately after we started, it became very clear that it is very accessible and very user-friendly. It is a managed service. It is not like you set up a SQL and you need to do everything yourself. It is a managed service, and you have global distribution automatically. You set a checkbox, and you have a globally distributed database with high availability and continuous backups set up. It takes away a lot of the pains that you encounter as a startup company that needs to interact with enterprise customers. Our target audience is enterprise B2D customers who have specific requirements around data residency, backup and restore, high availability, and so on. Azure Cosmos DB makes it very easy to comply with those requests.

The flexibility and scalability are valuable. You have multiple models. You have serverless, and then you have provisioned throughput, auto-scale throughputs, and so on on top of reserved capacity possibilities where you prepay for capacity. I like that. It gives us a lot of flexibility. The scaling is instantaneous as well. You immediately have all the resources available. The fact it is NoSQL makes it powerful. 

Resource governance across tenants is something that requires some work. There is some room for improvement there. We are a multitenant solution. We decided to follow a certain approach in our architecture, which had an impact on the Azure Cosmos DB. There are multiple approaches to implementing multitenant architecture on Azure Cosmos DB, but there is still no single or best-recommended approach when you have a big variance in the size of your tenants. That is something that still needs to be worked on.

The monitoring aspect can also be better. There should be better monitoring of the costs versus the performance. That is sometimes difficult. It is easy to see or track performance monitoring and separately track your bill, but it is difficult to view the overall picture in terms of the relationship between the cost and the performance. That is something they still have to work on.

We have been using Azure Cosmos DB since August 2018. It has been a bit more than six years.

I would rate it a ten out of ten for stability. We did not encounter any downtime. We never encountered any drops in latency. It is a very stable product.

It depends on how deep your pockets are, but it is very flexible. If you have a good architectural setup, you can easily scale with it. Scaling is almost instantaneous. It is pretty flexible.

I have interacted with their support. If I have issues, I log a support request with Azure, and then it goes via Azure. If I have architectural questions and so on, I already have a lot of contacts within the Azure Cosmos DB product team. I can contact them to get a better understanding. They are very reachable. Most of the time, I get an answer within a few days. I would rate their support an eight out of ten.

Positive

It is a cloud deployment. Its initial deployment is easy. You set up the Azure Cosmos DB instance. It takes a few minutes. One person can definitely set it up.

The time taken by a team to be onboarded with Azure Cosmos DB varies. It depends a bit on whether the user has any experience with NoSQL or not. If he has experience with NoSQL, it would take a few days or months to get up to speed and understand how to use the platform in a day-to-day fashion. There are also advanced features and concepts. For example, if you are using SQL Server, not everybody understands to the fullest how a cluster index works behind the screens, but they do know how to use a query or how to write a query, so there is a difference. Writing a query and so on takes a few days, and that is it. Understanding the concepts of partitioning, such as logical partitions, physical partitions, scaling on those partitions, the quota requirements, high availability and so on might take a few weeks, which still is not that much.

Once you are used to the concepts of throughputs, scaling, or request units, it is easy. In terms of the learning curve as a whole, it is not the easiest, but it is right above it.

Its maintenance is all being taken care of by the Azure Cosmos DB team.

It is hard to say if Azure Cosmos DB helped decrease our organization’s total cost of ownership because we started with a greenfield application. We built something from scratch and immediately started using Azure Cosmos DB. However, there have been two features that have created an impact on TCO. These two features that were released helped to not increase our TCO in one-to-one correlation with the number of customers we have. We have the auto-scale functionality, which is two or three years old now. It made a big difference in the cost. The second one is the dynamic per partition and per region auto-scale functionality. We enrolled in it during a private preview, but it went GA just recently. That decreased the bill as well.

It is expensive. The moment you have high availability options and they are mixed with the type of multitenant architecture you use, the pricing is on the higher end.

We had a look at MongoDB but decided not to use it because the managed service of MongoDB was not so powerful compared to Azure Cosmos DB. You still have a DIY approach with MongoDB and you set up everything yourself, but as a startup, your resources are limited, so you do not want to spend time on setting up the infrastructure.

We also had a look at Postgres. I have a few options in Postgres to do NoSQL, but the actual NoSQL power of Azure Cosmos DB really makes a big difference. We could not find a better solution for that.

We do not use the built-in vector database capability. At the moment, we do not use anything for that. We do use all change feeds, all versions, and deletes to link with Microsoft Fabric to populate the data warehouse. We do not use mirroring yet because mirroring has a few limitations. That blocks us from using it.

Azure Cosmos DB has not helped us to improve the search result quality in our company. That is not something of importance in our application. It is an ERP application.

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

We utilize the solution for big data, which is collected from IoT devices and streamed through a number of Azure services. The data is then landed in the Cosmos database for analysis later.

The solution is used because we get faster response times with large data sets than with SQL. It's essential for us because we have half a billion rows, and we need to return them quickly. 

Using it is easy. We are having trouble optimizing it. I'm not a technical person, so I couldn't explain why, but we're not getting the performance we were expecting. I'm sure it's probably an us problem instead of a product problem, but that's where we are.

From about half a billion rows, we're returning maybe 20,000 in two or three minutes. We don't know why, but we are working with Microsoft and a third party to figure that out. 

I have used it for about four years.

There are issues with latency between data arriving in Cosmos and showing up in a query. I'm told that's just the nature of the way Cosmos works. It can take up to five minutes to show up, but that's not a significant issue as we have workarounds in place.

I think the ability to scale workloads will depend on the outcome of tomorrow's meeting.

The customer service is lacking. We have a premier support agreement, but support is hit and miss. There are good engineers and not so good engineers. Premier Support has deteriorated compared to what it used to be, especially for small to medium-sized customers like ours.

Neutral

We used SQL previously, but Microsoft told us to use Cosmos DB because SQL wasn't performing.

The initial setup wasn't a long process. It took a couple of weeks. The whole thing was a proof of concept that eventually migrated into live use.

The solution was a new product, so we didn't have a cost of ownership before. The cost has not surprised us. It's not been an issue. If we were doing multi-master replication globally, the cost would increase significantly, but since we're not, it's manageable.

I rate Microsoft Azure Cosmos DB seven out of 10. If we can fix the problem we have, I could rate it a ten because there's nothing else I can point to for improvement if the performance meets our needs.

We use Cosmos DB in a multifaceted manner, primarily as an operational data store. Additionally, it serves as an analytics and reporting synchronization platform through Synapse Link, Azure's connective data warehousing solution. By connecting directly to Cosmos using the change feed, we project data into our data warehouse and data lake, facilitating both operational functions and analytical reporting needs.

Cosmos DB significantly enhanced our search result quality due to its impressive performance and reliability, bolstering the overall quality of our service offerings.

Realizing the full benefits of Cosmos DB took time, roughly one to three months. This was mainly due to the transition from a relational data store and the need to restructure our data to fully leverage Cosmos DB's capabilities, such as change feed and other features. While the immediate benefit was eliminating infrastructure maintenance and reducing DevOps/SRE overhead, achieving the total value we sought required optimizing our data structure for this new environment.

We primarily use Cosmos DB because it's a managed platform service, eliminating concerns about hosting and reliability. Its geo-redundancy feature allows us to share data globally across three data centers in US Central, East US, and West US, with US Central as the primary write region and the others for reading. Additionally, we leverage Cosmos DB's auto-scaling features, including burst capacity, database-level, and collection-based auto-scaling, and dynamic scaling per region/partition, to accommodate our fluctuating workloads throughout the day.

Cosmos DB has a couple of areas for improvement. Firstly, the lack of multi-collection joins is a significant limitation. Secondly, Azure Synapse Link, their data warehousing and synchronization feature, is unreliable and still feels like a preview feature. Improved reliability in this area would be greatly appreciated. Additionally, while Microsoft provides helpful internal monitoring tools, the managed nature of Cosmos DB can sometimes hinder visibility and make it difficult to troubleshoot issues, leaving us unsure whether the problem lies with our implementation or the service itself. Overall, we are satisfied with most aspects of Cosmos DB, but addressing these issues would significantly enhance its usability.

One of the primary challenges with Cosmos DB as a non-relational data store is the careful data modeling required due to the lack of collection-level joins when using the SQL API. While joins are possible within a single document, joining across documents or collections is not supported with this API. Although the Mongo API and Gremlin API on Cosmos DB allows for cross-collection joins this limitation in the SQL API remains a significant drawback.

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

While Cosmos DB has experienced occasional stability issues in previous years, its performance has been consistently reliable over the past 12 months.

The scalability of Cosmos DB is very good. It is one of the best capable offerings for scaling workloads.

We pay for unified response support, and generally, the support for Cosmos DB is good. However, without top-tier support, it can take a while and might not always be the most helpful.

Neutral

We extensively use Google Bigtable and, to a lesser extent, MongoDB. Additionally, we are increasingly utilizing Redis for various NoSQL use cases.

Cosmos DB and MongoDB differ in several key areas. While Cosmos DB excels in scalability, reliability, especially on Azure, and change feed capabilities, MongoDB offers a superior developer experience with local development options and more complex NoSQL use cases like multi-collection joins and advanced store procedures. However, MongoDB's query workloads are generally more capable, and it offers a wider range of indexing options and document size limits. Ultimately, the choice depends on specific needs and priorities, with Cosmos DB favouring cloud-based applications and MongoDB providing greater flexibility for complex database operations.

The initial setup was straightforward; one person completed it within one week.

We handled the deployment all in-house without any external integrator or consultant.

Cosmos DB is a managed offering, so its cost is understandably higher. However, the value it provides aligns with its price, especially considering the discounts we receive. By purchasing reserved units for three years, we secure a significant discount, making the cost justifiable for our needs. Without this discount, the list price might be prohibitive for certain use cases.

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

It took us three months to be fully onboarded with Cosmos DB.

The learning curve for Cosmos DB is certainly different from SQL databases. While most developers become proficient with basic functionality within a week or two, achieving true expertise in Cosmos DB requires a considerably longer time investment due to its unique architecture and features.

The maintenance is handled by Microsoft.

Be very careful with your partition keys when using Cosmos DB.

We use it primarily to log all events for a particular user and product. A particular users are logged in to see if a product has been modified. If someone modifies the data, we log that information along with the email. This helps when we need to compare modifications to a product.

Our admin section benefits greatly as Cosmos DB makes it easier to track down the history of product modifications, including the initial price, the current price, who modified it, and how much it was modified.

The search is slower than SQL but faster than MongoDB and other document databases.

Cosmos DB is a document database that stores data in JSON format for faster retrieval of unstructured data. I personally appreciate the speed, which is significantly better for unstructured data, especially since Cosmos DB had JSON as a data type early on.

It's pretty easy to use and optimize since it's unstructured data. It sometimes takes time since it's in JSON format, but it's useful in the admin section. The learning curve isn't long if you have some SQL knowledge because the queries are similar. It's straightforward for anyone with database exposure. 

We don't use the vector database, but we're aware of it and we know that it will allow faster retrieval with Azure AI integrated.

The UI needs enhancement. Unlike SQL, Cosmos DB's UI is not as straightforward, making it a bit challenging to use efficiently.

I have been using Cosmos DB for the last seven years.

We have not experienced any downtime. The retrieval is significantly faster compared to using SQL for storing JSON data.

We have had no issues with scalability. It works well for us, fitting seamlessly into our workflows.

We evaluated DocumentDB and other document databases, but since Cosmos DB is a Microsoft product and integrates well with Azure, it was the preferred choice. 

Like any technology, it took a little time to learn Cosmos DB. It was relatively straightforward. We had to watch a few videos on how to set up particular databases, indexes, and keys. 

Cosmos DB has definitely improved our organization's cost structure, but I would need to check the specifics to provide exact numbers.

The Cosmos DB pricing model is reasonable, especially since we use it for backup operations rather than front-end processes. We have been using it for several years and continue to do so.

We compared Cosmos DB with DocumentDB and other document databases.

I would rate Azure Cosmos DB an eight out of 10. There is potential for improvement, especially in the UI, which can be cumbersome to navigate.

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.

I was using Microsoft Azure Cosmos DB to store unstructured data.

It is easy to learn how to use Azure Cosmos DB.

Azure Cosmos DB enhances search result quality by enabling rapid data retrieval within its collections. It can handle large amounts of data efficiently.

Azure Cosmos DB offers numerous advantages, including flexibility, cost-effectiveness relative to performance, and a pre-existing infrastructure on Azure. Its support for multiple data models, extensive document database capabilities, and fully managed maintenance provided by Azure make it a compelling choice with immediately apparent benefits.

Microsoft Azure Cosmos DB simplifies the process of saving and retrieving data. The only requirement is to create the collection and streamline data management.

There should be a simpler way for data migration. Currently, we need to write scripts to update data in bulk and ensure proper connectivity for migration with .NET, which seems hectic and risky.

I have used Cosmos DB in my previous project for around one year.

Azure Cosmos DB offers high availability, at approximately 99.9 percent, with good latency.

Azure Cosmos DB handles scalability well. It is easy to scale up the workloads.

Dynamic scaling enhances cost-efficiency and usability by automatically adjusting resources to meet demand. This means the system scales up capacity when needed and scales down when not in use, preventing unnecessary expenses.

I have used MongoDB, but not extensively. If working with Node, I would recommend Mongo, but in a Microsoft environment, I recommend Cosmos.

The initial deployment was manageable. It took around five to seven hours to deploy Cosmos to a working condition fully.

At that time, we had a team of four people managing the infrastructure and deployments.

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

New users should be familiar with DocumentDB since most people are only aware of relational databases, but Cosmos is different.

I have been using Microsoft Azure Cosmos DB for the last five years for IoT-based data saving and other purposes. We use non-structural data for various reasons. For instance, we are using artificial intelligence to save multiple data sets coming from different sources.

It is a managed service, so we do not want to worry about other aspects.

One valuable feature of Microsoft Azure Cosmos DB is partitioning. Its performance is very nice. I use it mostly on the Microsoft backend, particularly .NET and .NET Core technology. From deployment and accessibility aspects, there is significant performance improvement.

Additionally, consistency is noteworthy. For example, we have people spread across multiple locations. If they update data in Australia, we can access it in another location within a fraction of a second. That is an impressive feature of Microsoft Azure Cosmos DB.

It is very good from the optimization and usage point of view. It is very user-friendly. Microsoft also provides support from the performance aspect. They support us from the optimization and scalability aspects.

The query searching functionality has some complexities and could be more user-friendly. Improvements in this area would be very helpful.

We have multiple applications. Our applications are running in different environments such as AWS and Azure. We are able to give flexibility to AWS to access this data from Microsoft Azure Cosmos DB. We have created an interface between them through APIs. Through the APIs, the AWS applications can consume the data from Microsoft Azure Cosmos DB, but we have seen some slowness or latency, whereas with Azure, we see better performance. Our AWS is in the Eastern zone, and people in the Western zone have some latency.

I have used the solution for five years.

We are seeing some latency issues with AWS. It offers good availability. 

Being serverless, the scalability is very good.

We pay for the support. We are happy with their support. If we face any challenges initially, they provide us with a resource to answer all our questions.

Positive

Previously, we used MongoDB and DynamoDB, though not extensively. Because of client preferences and their use of Azure, we chose Microsoft Azure Cosmos DB. DynamoDB uses clusters, which affect costs.

The initial setup can be somewhat tedious. We have to set up things, run them, see the results, and fine-tune them.

The initial setup took more than one month. After that, everything became automated. Now, if we want to deploy it in another location, the operational team typically takes one week. They verify whether everything is working properly or not. By using the automated scripts, we can deploy it at other locations.

We have a separate team for configuration. We also get support from Microsoft.

Its price is in the middle, neither too low nor too high.

We are happy with the usage of Microsoft Azure Cosmos DB for our use case. In terms of learning, it is of medium complexity. It is neither very tough nor very easy.

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

I develop applications. I developed an application where I had to search the Azure Cosmos DB database for values related to suspicious entities. It involved retrieving, sorting, and manually searching data through queries.

Azure Cosmos DB helped improve the quality of our search results. We could see its benefits immediately after the deployment.

The availability and latency of Azure Cosmos DB are excellent. It handles large data insertions efficiently without any problems related to scalability. It scales workloads very well.

The library of Azure Cosmos DB is like JPA, but it is not exactly JPA. We could not integrate that.

The size of the continuation token in Azure Cosmos DB should be static rather than increasing with more data, as it can lead to application crashes. They should use a static key size.

If we want to update some data, we cannot use the SQL command line. It is not like SQL Server or any other relational database. We have to send the JSON file or send the text to the Azure portal.  These are the only two options. We cannot use the normal SQL statement.

I have been using it since December 2021.

The scalability is very good. We performed performance tests, inserting objects with more than 10,000 records without any issues, although, on the application side, we started to see high memory consumption. That is because, with larger JSON files, you will have more objects in the Java application. These things consume memory, but there are no issues regarding scalability.

I have not contacted their support.

Neutral

I have experience with MongoDB but only for personal studies. I only learned the basic things.

It was easy because we have Terraform embedded in the Jenkins pipeline. Once I deploy the application, it connects with Azure Cosmos DB. It is already configured, so I do not have to worry about this part.

It took us about one month to get onboarded and understand the basic functionalities. 

It does not require any maintenance at our end.

We usually have a team of three to four people.

I am not aware of the price, but a challenge that I have faced occasionally is that running longer queries requires more RUs, so I have to ask someone with permissions to execute the queries.

I would advise learning more about queries and select statements. You can use that on the Java side and Cosmos SDK.

It is easier to learn if you already know relational databases. You can use some of that knowledge to work with Azure Cosmos DB. Also, if you know JPA, it would not be so difficult to work with the Cosmos SDK for Java application development. Inserting data is also simple.

It is at a medium level in terms of ease of use. There is documentation for gathering the information. Azure Cosmos DB does not have any constraints for the column names. If you want to create a specific query, you can find information related to that in Microsoft documentation. You can find queries to solve specific problems.

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