What advice do you have for others considering Microsoft Azure Cosmos DB?

In my opinion, the main difference between Microsoft Azure Cosmos DB and other types of databases is hard to say. It's mostly how developers see everything, as it depends more on the development side—what they want to use and what features they need, such as relational databases or document databases. This leads us to select the right database based on those inputs. The selection is based on the use case. We are implementing other Microsoft Azure solutions like Azure SQL and Postgres. We focus only on Microsoft Azure and do not work with other vendors like AWS. I gave this review a rating of ten out of ten.

From what I have used, I believe the tool is quite good. Microsoft Azure Cosmos DB is currently quite good, and I do not have any enhancements I would recommend since I am not a heavy user, having used it for about six months. My advice to other companies considering Microsoft Azure Cosmos DB is to simply try it, and you will love it. I would rate this product a 9.5 out of 10.

I am not using Microsoft Azure Cosmos DB for AI or real-time applications yet. I have not used Microsoft Azure Cosmos DB's built-in vector or hybrid search. I assess Microsoft Azure Cosmos DB's ability to search through large amounts of data by saying if I provide it the right information, it does really effectively; otherwise, if I can't provide it the partition key, then it's much more painful. I have integrated Azure Functions with Microsoft Azure Cosmos DB. I assess the overall experience of using Microsoft Azure Cosmos DB with these services as good; it's worked really effectively. I have not had any problems with the integration of Azure Functions and Microsoft Azure Cosmos DB since it's pretty straightforward. I would describe my team's experience developing with Microsoft Azure Cosmos DB SDKs and APIs as good; we've had pretty good results with it. We're starting to try managed identity; we haven't used that yet, but we're moving to that in the next couple of months. However, we haven't had any trouble with it at all. Microsoft Azure Cosmos DB's dynamic scaling has helped decrease my company's overhead costs. I would say it has decreased costs by at least ten percent to a quarter. My advice to other companies considering Microsoft Azure Cosmos DB is to be prepared for the fact that DocumentDB is the trickiest thing, especially getting used to the partition keys, but other than that, the replication and everything is very handy and makes it easy to manage. I would rate my overall experience with Microsoft Azure Cosmos DB as a nine out of ten.

I am using Microsoft Azure Cosmos DB for both AI and real-time applications. I have not used Microsoft Azure Cosmos DB's built-in vector or hybrid search because our clients prefer to use MongoDB with the RU feature. Therefore, I do not use Vector DB in production; however, I have utilized the Vector Database in simpler cases based on my experience. In other scenarios, we resort to AI Search because our clients opt for Azure AI Search. In terms of my team's experience developing with Microsoft Azure Cosmos DB SDKs and APIs, I primarily use only the SDK and not the APIs, as the SDK fulfills all our requirements for the deployment lifecycle. I rely heavily on the SDK, for example, to create indices within a container during production maintenance and for configuring the release of our code to production. Creating indices and managing the container is very simple with the SDK, which I find is excellent for Microsoft Azure Cosmos DB for NoSQL, although I notice that the Mongo SDK does not integrate as effectively for this type of database. Microsoft Azure Cosmos DB's dynamic scaling has indeed contributed to decreasing our operational overhead costs. My advice to other companies considering switching to Microsoft Azure Cosmos DB is that the SDK makes it very simple to create a container or a database, and managing it, including backup and restoring to a previous data point, is very straightforward. I rated this product with a nine out of ten.

I wouldn't know how Microsoft Azure Cosmos DB can be improved because I don't think we use enough of it; I need to learn more about what to use in Microsoft Azure Cosmos DB. I find the pricing transparency of Microsoft Azure Cosmos DB to be a little confusing, but we're figuring it out. I would recommend Microsoft Azure Cosmos DB to another organization that's considering using it. I gave this review a rating of nine.

Microsoft Azure Cosmos DB is where we're going next for AI or real-time applications such as Copilots, personalization, and recommendations. Right now, the data itself is pretty dynamic, but the output is pretty static. The idea is to take that and make it more dynamic so that instead of creating a report, you can ask the AI specific questions about the data, and it should be able to build those reports for you. The primary reason for switching to Microsoft Azure Cosmos DB was to try something new, which may sound unconvincing, but we wanted to see if it could prove out. There were also cost savings and optimization opportunities that seemed promising for our new projects. My advice for someone who is considering Microsoft Azure Cosmos DB is to do some testing with it and give yourself time to think about your data in a different way, as it's a different concept from SQL. For us, it has worked out pretty well for a lot of unstructured data. I would rate this solution an 8 out of 10.

I rate Microsoft Azure Cosmos DB a 9 out of 10 because there is always room for improvement in any software. The benefits of Microsoft Azure Cosmos DB were immediate for us. It was within our budget, and we cannot say it constrained our finances because it was approved. The cost-benefit analysis shows that the benefits outweigh the costs. The maintenance costs are also within our estimated budgeted projections as a company. I am willing to provide references for Microsoft Azure Cosmos DB and can be a reference for anyone interested in purchasing the same product. I am available to be contacted by Microsoft regarding this review should they have any questions.

In my previous company, we were partners with Microsoft about six or seven years back. Currently, we are just customers, and the same holds true for my current company as well. I would rate Microsoft Azure Cosmos DB an eight out of ten for everything.

Vector database capabilities exist in Microsoft Azure Cosmos DB. They allow us to store vectors in NoSQL or other containers such as tables, although we only utilize a few features. While it is possible to store vectors, we can’t search for them directly since vectors have multiple dimensions; thus, we can only store them. Microsoft Azure Cosmos DB has helped improve our search result quality, although there is no perfect searching technique here. We need to utilize SQL queries, which are not standard SQL queries, requiring us to create specific SQL queries after reviewing the documentation. This includes reading samples provided in the documentation to access the data effectively. I would recommend Microsoft Azure Cosmos DB to others who seek solid security and wish to protect their data without exposing keys and values. API-based authentication mitigates risks associated with lost keys. Using service principals and managed identities is advisable for enhanced security. However, if the effort to manage keys is unappealing, I suggest exploring other platforms that may not offer the same level of data security. Overall, I would rate Microsoft Azure Cosmos DB a seven out of ten.

I would recommend Microsoft Azure Cosmos DB to other users due to the latest dynamic data masking feature. If a company's infrastructure primarily uses Azure products, it makes sense to use Microsoft Azure Cosmos DB for better integration. However, if your business requires a very stable DB, you should explore other options as we faced some stability issues. On a scale of one to ten, I rate Microsoft Azure Cosmos DB a seven overall.

My advice to people considering using Microsoft Azure Cosmos DB would be that if they are using Azure and need a native solution, it is a nice choice. If they use MongoDB, they would need some APIs to integrate. As it is our first time using a NoSQL solution inside the company, we will probably continue using Microsoft Azure Cosmos DB. I would rate Microsoft Azure Cosmos DB a ten out of ten.

We do not utilize the built-in vector database capability yet, but we have plans to. I would recommend Microsoft Azure Cosmos DB to other users. I would highly recommend digging into the details of how it works behind the scenes and discussing with the technical team prior to implementation to avoid mistakes that could lead to a gigantic invoice at the end of the month for nothing. Ensuring a good understanding of how it all works. Overall, I would rate Microsoft Azure Cosmos DB as an eight out of ten.

I have no complaints. It does its job efficiently and is easy to set up. Our organization has been using it for quite some time. They must see a value in it. Otherwise, they would go for a better technology in terms of performance or pricing. I would rate Microsoft Azure Cosmos DB a nine out of ten.

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 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.

It is not like a traditional database. Choosing the partition key needs an understanding because it will affect the database speed. By making your partitions in a logical and efficient way, you can improve the speed of search analysis. I would rate Azure Cosmos DB an eight out of ten.

I would recommend this solution. For e-commerce applications, it is more beneficial because it can store semi-structured data. It is the best option if you want to get data quickly because it organizes the data in a good way. When a region fails, it automatically switches to a healthy region. It has backup storage, and it scales automatically based on the peak time or low time. I would rate Microsoft Azure Cosmos DB an eight out of ten. It is a good solution, but the cost can increase with cross-partition queries due to data distribution.

I would recommend Microsoft Azure Cosmos DB if you are looking for performance. I am not sure about the pricing, but if you have a large number of users, Microsoft Azure Cosmos DB is helpful. If you are using proper indexes, data retrieval is fast and search is easy. Otherwise, it will take a lot of RUs to get the results. If you are migrating from traditional or legacy workflows to Microsoft Azure Cosmos DB, it would require a lot of rework. For new implementations, Microsoft Azure Cosmos DB is advisable. I would rate Microsoft Azure Cosmos DB a nine out of ten.

Its learning curve is a little bit steep for those who are new. If you have a little bit of experience in infrastructure and databases, becoming familiar with Azure Cosmos DB does not take much time. It is easy to use if you have knowledge of NoSQL databases in general. If you know how to create schemas, then setting up the infrastructure in Azure Cosmos DB is no hassle. The basic requirement is to know about databases. That is it. Many things are managed by default in the Azure platform. You just need to take care of the specifics of your project and the regions you will be working in. These are the things that are automatic in Azure Cosmos DB. I would rate Azure Cosmos DB a seven out of ten, considering its ease of use, efficiency, and provision for peace of mind through its features and functionalities. There is still room for improvement, particularly in pricing and feature offerings.

I rate Cosmos DB eight out of 10.

I rate Cosmos DB nine out of 10.

I rate Cosmos DB eight out of 10.

I rate Azure Cosmos DB eight out of 10. There is always room for improvement, and the company could develop new features that could make it even better, but I am very satisfied with the current performance.

I would rate the product an eight or nine out of ten. We are very happy with it as it runs smoothly right out of the box.

I rate Microsoft Azure Cosmos DB nine out of 10. To avoid migration challenges, data storage methods in Cosmos DB should be carefully considered.

I rate Microsoft Azure Cosmos DB seven out of 10.

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.

I would rate Cisco Email Secure about seven out of 10. Get used to the user interface and always click the save button to ensure changes are applied.

I rate Azure Cosmos DB as nine out of 10. The product is fit for purpose and performs well,

I would rate Microsoft Azure Cosmos DB eight out of 10.

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.

I would rate Azure Cosmos DB an eight out of ten. There is room for growth, but Microsoft is constantly releasing new features and moving very fast.

I rate the product as eight out of ten.

I would rate Cosmos DB as an eight out of 10 for its overall capabilities, responsiveness, and alignment with our needs.

I would rate Microsoft Azure Cosmos DB a nine out of ten. There is always room to grow, but it is a highly capable solution. I am looking for more opportunities to use it as we help customers move toward more cloud-native technologies, rather than always defaulting back to what they are familiar with, which is sticking with Microsoft SQL Server or Azure SQL.

I would rate Cosmos an eight out of ten. Be cautious about spreading out the load evenly, especially when dealing with large volumes to prevent getting errors.

I would rate Microsoft Azure Cosmos DB a nine out of 10. Both Microsoft Azure Cosmos DB and Cosmos SQL DB are familiar to our developers who come from a SQL Server background.

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.

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.

I rate Cosmos DB ten out of ten. While there is a learning curve when transitioning from traditional SQL databases to NoSQL databases, this is not specific to Cosmos DB. Regardless of the provider, understanding NoSQL requires a shift in approaching data storage and retrieval, particularly for those familiar with relational databases. This inherent learning curve stems from the fundamental differences between the two database types and necessitates learning new concepts and techniques for effectively working with NoSQL databases. No maintenance is required. For newcomers, it is crucial to understand that despite the SQL API, Cosmos DB is not a traditional SQL database. Many issues arise when trying to apply relational database principles. Understanding NoSQL databases' limitations and adapting to the mindset required is essential.

To new users, I would advise first knowing their data. They should know whether it fits their solution, which Azure Cosmos API to use, and what scale they intend to run it. I would rate Microsoft Azure Cosmos DB a nine out of ten.

I would rate Microsoft Azure Cosmos DB eight out of ten. Understanding some of the subtleties of Microsoft Azure Cosmos DB can take time, and some individuals at ASOS still find concepts like partitioning unclear. However, getting started with Cosmos DB and developing functional applications is quick and can be achieved in a short timeframe. We require minimal maintenance to validate that we've configured, for example, the correct indexing policies as required by our queries. New users should make sure they understand partitioning because once it's selected, it is difficult to change it. Otherwise, you would need to migrate everything over to another collection.

I 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 rate Microsoft Azure Cosmos DB ten out of ten. We use Azure Cosmos DB extensively for searching alongside Azure AI Search, which offers full-text Lucene syntax-compatible querying. While a significant portion of our searches leverage these dedicated search indexes, we still conduct a fair amount directly in Azure Cosmos DB. Although it might not be entirely fair to say that searching isn't Azure Cosmos DB's strong suit, it's worth noting that its capabilities are constrained by partitioning requirements. This limitation places a ceiling on its overall effectiveness for specific scenarios. While Azure Cosmos DB can be extremely valuable for querying within partitions, alternative solutions are often better suited for queries spanning multiple partitions. I've built tools around the Azure Cosmos DB SDKs to make them incredibly easy to use. My team had no learning curve and could leverage our shared libraries. It took me less than a week to achieve a production-quality implementation for accessing and saving data within a platform. We have 20 people in the organization who interact with Azure Cosmos DB, consisting of 15 engineers and five others. Azure Cosmos DB typically requires minimal maintenance, but if data partitioning is not done correctly, some overhead may be incurred due to the need to replicate containers and move data. Thus, while generally low maintenance, some maintenance can be required in certain situations. For anyone thinking about implementing Azure Cosmos DB, first, understand your data and invest time in understanding the partitioning in Azure Cosmos DB. If you get your head wrapped around the partitioning, everything else will be straightforward.

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.

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.

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.

Cosmos DB is a good option if someone is looking for a NoSQL database. Overall, I rate the product a nine out of ten.

Microsoft Azure Cosmos DB is deployed on-cloud in our organization. I would recommend Microsoft Azure Cosmos DB to other users. Overall, I rate Microsoft Azure Cosmos DB a seven out of ten.

I rate Microsoft Azure Cosmos DB an eight out of ten. It is useful to store original data in original format.

If a customer needs to store JSON data, and the solution doesn't require complex structure and reporting like BI reports and RDBMS, opting for a NoSQL database could be ideal. NoSQL databases are suitable when data isn't structured in a relational manner and when extensive normalization isn't a priority. For efficiently handling JSON data for UI purposes or other needs, a NoSQL database like Cosmos DB is the way to go. However, in the NoSQL landscape, various options like Redis DB, CouchDB, MongoDB, and Cosmos DB exist. If a preference leans towards Microsoft technologies, then Cosmos DB becomes a logical choice. Comparing Cosmos DB with alternatives like Redis DB is advisable before making a final decision. Thus, my typical recommendation involves considering these factors. I would Cosmos DB a nine out of ten.

I would overall rate the solution an eight out of ten.

I would recommend understanding the underlying databases like Cosmos DB, but I don't think it supports Oracle. However, it does support various other databases. If it supports the databases you need, then go for it. If it doesn't support them, there's not much you can do. Overall, I would rate the solution an eight out of ten. I'm not giving it a higher rating because it doesn't support all databases.

It's a highly scalable, highly robust, and very user-friendly solution. It is easy to set up; the most important point is that it is on a cloud. The solution is also very easy to deploy. Only some connectivity features need to be developed. I give it an eight out of ten.

If your existing infrastructure already uses Microsoft services or is more of a Microsoft-dependent solution, it's best to be on Microsoft Azure cloud. This is because it integrates very well, and there is a smooth integration with other Microsoft products that are already running on our products. You can also leverage some of your existing licenses, saving you a lot of costs when you move to the cloud. That's one solution I would suggest for anyone who is moving from on-premise to the cloud. Overall, I would rate the solution an eight out of ten.

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.

I would rate this solution as eight out of ten. The APIs are improving and are easy to use. It is easy to set up a new database, and the auto scalability and support for different models are good features.

I rate the solution a nine out of ten.

I would recommend this solution to others who are interested in using it. I would rate Microsoft Azure Cosmos DB a seven out of ten.

My general advice to anyone looking to implement Microsoft Azure would be to start small. When you see your application increase or your traffic increase on site, you can slowly scale. I would rate the solution a seven out of 10 overall.

The cost is the biggest limitation of this solution. I would rate this solution a six out of ten.

I've been using Microsoft Azure Cosmos DB, a cloud DB solution. It's deployed in a cloud environment, on a public cloud with security for ourselves. My company is a partner of Microsoft and also a reseller. My advice to people looking into implementing Microsoft Azure Cosmos DB is that it would be good for them to use, specifically if they are looking for a NoSQL database to ingest the data and do data discovery using the data in a BI tool. It's easy to ingest the data and work with the data in Microsoft Azure Cosmos DB and understand that, because it is not a SQL database, which means it's not as structured. You can add data, and then do a data discovery, and use it the best way for you. I would recommend Microsoft Azure Cosmos DB. My rating for Microsoft Azure Cosmos DB is eight out of ten.

I would recommend this solution to others. I rate Microsoft Azure Cosmos DB a nine out of ten.

I rate Cosmos DB eight out of 10. I would recommend it for an appropriate use case. However, you need to be aware of the system's limitations. If you're using the DocumentDB system, it's crucial to plan properly for document structure, etc. You also need to plan for failure to ensure that your system can survive when any node fails. Set up clustering, redundancy, high availability, and so on.

I would rate Microsoft Azure Cosmos DB a nine out of 10.

This is a good product and I recommend it, especially in cases where people want to keep their information outside of the organization and on the cloud. I would rate this solution a nine out of ten.

I am using the latest version of the solution. Overall, I would rate the solution at an eight out of ten. I have always been very happy with its capabilities. I would recommend the solution to other organizations.

Overall, on a scale from one to ten, I would give this solution a rating of seven. Aside from the scalability issues, we haven't experienced any other issues. I would recommend Cosmos. It made our lives a lot easier. There's not a big learning curve in order to understand the structure and how to use it. We were good to go with only one container. Anybody who is new can learn quickly.

Before implementing, know now how to use DocumentDB. Understand your use case. From an architecture perspective, we have a use case where we wanted to use more SQ and we used DocumentDB as the first consideration. There isn't a better SQL than DocumentDB available. Cloud provides this type of platform. The automatic performance is also very good. We did research on the internet and decided to go with DocumentDB. I would rate it an eight out of ten. Not a ten because there is what to be done for improvement. In the future, it should be simplified for developers so that it's not a hassle for them. There aren't many resources for SQL and DocumentDB. It may take time for more documentation to come out.