Microsoft Azure Cosmos DB Reviews

The company is using Microsoft Azure Cosmos DB for business intelligence information, specifically for demand management.

What I like about Microsoft Azure Cosmos DB is that it's easy to do data ingestion and use the data in different applications. If you talk about business intelligence such as the Power BI tool, it's easy to connect because both are Microsoft products. With Microsoft Azure Cosmos DB, it's easy to connect and do data ingestion.

At the moment, because I'm still new in terms of using Microsoft Azure Cosmos DB, I don't have any feedback regarding areas for improvement in the product. So far, it has met all the expectations and needs of my company.

It would be nice to have more options to ingest the data, for example, more file options or more search options. Currently, you can use JSON, but if there were other file types you can use for data ingestion, that would be nice. This is the additional feature I'd like to see in the next release of Microsoft Azure Cosmos DB.

I've been using Microsoft Azure Cosmos DB for the past six months.

Microsoft Azure Cosmos DB is a stable product.

Microsoft Azure Cosmos DB is a scalable product.

We have a partnership with Microsoft, so the response time of the technical support team for Microsoft Azure Cosmos DB is really good at the moment.

Microsoft Azure Cosmos DB was easy to set up.

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.

Microsoft Azure Cosmos DB can be used for various purposes. The query language used for Cosmos DB is very similar to SQL, which gives it an advantage. It's a globally distributed multi-model database service, meaning it supports multiple data models, including documents, key-value pairs, graphs, and time series data models.

It's highly scalable and supports consistency, security, and multiple security options, such as REST and transit encryption. It also provides automatic support for these options. These are some top-level benefits of using Cosmos DB, making it a highly versatile and useful tool.

The multi-model database is the most valuable feature. 

One thing that concerns me is the cost, especially for smaller workloads. Cosmos DB is a little more expensive than other database services, particularly if you have tight-traffic models. However, it does have a few advantages, such as being a multi-model database. The biggest problem is the learning curve and other database services like RDS. 

Additionally, advanced analytics capabilities like real-time analytics and machine learning are not embedded in Cosmos DB. Vendor lock-in is a big concern. Cosmos DB is a proprietary database service offered by Microsoft that might not be compatible with other databases. 

I have been using this solution for three years. I am using the latest version.

From a stability perspective, it's a pretty robust solution designed to offer high availability and fault tolerance. It provides multiple levels of redundancy and automatic failover to ensure data availability and reliability. 

It is a scalable solution and has built-in backup and recovery capabilities. We developed it for one of our clients with around 20-25 users.

When compared to other cloud platforms like GCP and AWS, I think Microsoft needs to work on its tech support.

There is some learning curve associated with this software. It becomes relatively easy to implement if you have an expert to work with. 

The deployment process and maintenance depend on the size of the product and what you're trying to migrate. Generally, one cloud solution architect and one big data developer with Azure experience should be sufficient.

We could see an ROI. The whole idea of migrating to the cloud was for a better ROI, and we can see that now that the customer has moved to the cloud.

As your data grows, the licensing cost can be expensive.

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 use Cosmos DB for geolocation identification.

If you pass by a market on the street, it sends me your location. My application will then send you a personalized notification about relevant products you can buy at the market.

The best feature is the velocity of making queries. 

I would like to see better documentation for this solution. 

The pricing of the solution should be reduced. 

I have been using Cosmos DB for one year. 

I would rate stability an eight out of ten. 

I would rate scalability a five out of ten. 

I would rate technical support a ten out of ten. 

The initial setup is straightforward. It's very easy to start a function or application with Azure. 

The solution is very expensive. 

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

We use Microsoft Azure Cosmos DB for faster databases.

The most valuable feature of the solution is that it is scalable with multiple master files. The performance of the solution is also good.

Microsoft Azure Cosmos DB's pricing model is complicated, which people don't understand. So, the pricing model should be improved.

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

Microsoft Azure Cosmos DB is a stable solution.

Microsoft Azure Cosmos DB is a highly scalable solution, and 15 to 20 people use the solution in my organization.

I rate Microsoft Azure Cosmos DB’s technical support an eight out of ten. They do take some time to get to the root of a problem.

Positive

The initial setup is straightforward and needs a few tools to upload.

We have had an ROI with the solution.

The pricing model of Microsoft Azure Cosmos DB is a bit complex.

We are using Microsoft Azure Cosmos DB as a cloud-based solution. Two admins and one manager are required for the deployment and maintenance of the solution.

New users must keep an eye on its pricing models and configure them properly.

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

Primarily, people do not have a clear understanding of the cloud and cloud services. Customers are a little bit scared about taking their data onto the cloud, and they think and they assume that it is not safe. So we just make them understand that databases or services on the cloud are more secure than on-premises infrastructure.

The performance point can be improved because when we run a search query on our on-premises machine and develop connectivity, a response comes in. But sometimes, the response gets delayed, and it can be due to network latency or something else we are yet to figure out.

Performance and high availability are two features I want to be added in the next release because that is the basic requirement of customers. Mostly, we have customers with the bank and banking institutions, and they want their databases perfectly integrated with the high availability feature.

I have been using Microsoft Azure Cosmos DB for less than a year.

It is a stable product. I would give it an eight out of ten. 

It is a quite scalable product. I give it a nine out of ten. There are more than ten users using this product. 

The initial setup was difficult. I would give it a six or seven out of ten.

The pricing depends on the budget allocated to the client; for some, it can be high, and for some not high. But mostly, when the prices are moderate, they are not very high. I would rate it a seven out of ten.

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.

I use the product for storing information related to our automation.

The product is simple to use. It is a NoSQL database. It is easy to commission the environment and use it.

It is not as easy to use as DynamoDB. The product always shows JSON-based documents. However, DynamoDB shows a table-based document. The documentation must be improved.

I used the solution a couple of months ago.

The tool’s stability is very good. I rate the stability a ten out of ten.

The tool is highly scalable. I rate the scalability a ten out of ten. We have more than 1000 users.

I used DynamoDB in my previous organization.

The initial setup is easy. The deployment could be completed within minutes.

The tool is not expensive. It is good for small use cases.

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

We use the product to input data that doesn't require structuring. 

Microsoft Azure Cosmos DB's most valuable feature is latency. 

The tool's pricing is expensive. 

I have been using the product since November 2021. 

Microsoft Azure Cosmos DB is stable. 

The product is scalable. My company has eight users. 

Microsoft Azure Cosmos DB's tech support is okay. 

Microsoft Azure Cosmos DB's deployment is straightforward. 

Microsoft Azure Cosmos DB's licensing costs are monthly. 

I rate the tool a nine out of ten. 

We have a database stored on Microsoft Azure where we fetch records and validate them against the application data that is displayed. We use it as a backend in an application to store data.

Within our organization, there are around 500 people using this solution.

There's no specific feature of Cosmos. Basically, we try to query the database. Since it's unstructured data, all the related fields are stored as a single JSON. You don't have to go ahead and hit multiple tables, or multiple containers. Instead, you get all of those things, relationally, stored in a nested JSON — in one single container itself. This way, you don't have to go back and forth to different tables. There are pros and cons with this feature, but often it makes things easier for us.

I am impressed with the TTL (time to live feature) of Cosmos. If we need to perform a manual addition to a container, we just need to configure the time to live. And then based on that, it can be deleted.

We should have more freedom to tweak it and make our own queries for non-traditional use-cases.

I have been using Microsoft Azure Cosmos DB for roughly one year.

Cosmos should be more stable. Sometimes we fetch more than 60,000 records at a time. When we try to fetch large amounts of data, sometimes it breaks-down, requiring us to rescale it. Overall, performance-wise, it needs to be a little more stable. 

I personally haven't had contact with the technical support. Our solution architects deal with support. 

I wasn't involved in the initial setup. We have a DevOps team that was responsible for handling the installation. We are just leveraging it for developmental purposes.

Cosmos should be cheaper. We actually intend to stop using it in the near future because the price is too high — and because of the stability issues. 

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.