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.