We are using the solution to build the model. We can create multiple models like the training data. It is a new user-friendly network. You can select the data from the UI page, which is more comfortable than programming. You can process the data within half an hour and find the best model.
We received millions of records for one project. We used Kafka to get the data into our application and then processed it through Azure, whatever data was injected. We wanted to process it and build the dashboard.
The data is handled manually. For example, if you were to do the same thing with Python, you'd have to check, rebuild, and deploy the entire thing. We should be able to change the data on the fly. We can make changes and immediately see the results appear on the screen. The best part is that you'd be able to convey to stakeholders who may need to be more technically proficient. By using the dashboard, you can convince your stakeholders.
Azure Stream Analytics is more user-friendly than AWS. With AWS, there are many components to manage, requiring strong technical skills for cloud usage. Suppose you have explicit domain knowledge and understand your use case. In that case, you should be able to use the product effectively. It's the real-time data streaming feature. You configure it, and it processes coming data. There are some use cases where you want to perform calculations in real time, like edge computing or when you need to make decisions based on the incoming real-time data.
Some features require logical thinking. For example, if you want to write an integrative custom script, then it will be more convenient. Automation is available.
I have been using Azure Stream Analytics for three months.
The product is 24/7 stable.
I rate the solution’s stability a nine out of ten.
The solution is scalable. It's reliable.
I rate the solution's scalability a six out of ten.
You can communicate via email, or someone will contact you. Sometimes, it might get delayed, but the support is good.
Positive
The initial setup is easy. It has more advanced structuring. For example, if your application runs on-premises, we have tools to migrate some applications to the cloud. If the maximum complexity of the desktop use case is very high, we have to consider various factors. We might estimate that within a week, we could complete the migration. Still, we also need to thoroughly check all scenarios to ensure they function correctly and whether they impact the user's experience. This thorough examination might extend the timeline to about one month. If the use case involves data migration and the application is already built to be cloud-compatible, then the process will take a little time. One to two weeks could be more than sufficient.
If the application is tiny, then even one person is more than enough.
I rate the initial setup a nine-point five out of ten, where one is complex and ten is easy.
The product is expensive but has stability and user-centric features. Those who seek comfort, regardless of cost, will choose Azure.
Some of our project customers are returning to us and mentioning AWS-related issues. It costs them more because whatever operations they conduct on AWS incur perpetual costs. Consequently, they opt for on-premises solutions. Therefore, people may revert to on-premises infrastructure if it is costly. Otherwise, most individuals prefer cloud-based solutions. Cloud computing is generally considered superior.
I recommend Azure Stream Analytics for handling large volumes of stable and huge data. Microsoft Stream integration adds significant value, making it a comprehensive solution. Azure Stream Analytics offers necessary features without unnecessary expenses for small organisations where budget is a concern.
Overall, I rate the solution a nine out of ten.
