Azure Stream Analytics Reviews

When it comes to doing partitioning for the data, if you are working with both ModiB and MongoDB, it's very powerful in Microsoft Azure - the clustering platform. If it's Spark Apache, Spark will be very powerful due to the fact that you have the option to have the Delta factory, which makes all that things available within the platform. 

It allows you to take a proactive decision for any abnormal functionality. For IoT, it will give you the facility to make quick decisions on any kind of metrics or output, et cetera.

The solution has a lot of functionality that can be pushed out to companies. There's a lot of analytics to take advantage of. What they have available on the market now is more than enough for companies to work with.

You can install the solution quite quickly and start using it right away.

While it depends on the business scenario, in some cases AWS offers better features. It's hard to speak to missing features at it really depends on the business case. However, in general, it has all the features a typical company might need.

The solution needs to be marketed better. Developers should be pushed or enticed to use the solution more to get it more well-known on the market. It needs more of a presence.

The solution offers a free trial, however, it is too short. You can't really properly test it before you have to start paying. They need to give companies a longer period of time to try it out risk-free. Also, the functionality is very limited. If you want to do a POC, you need the solution to offer more flexibility. Right now, you get a 14-day window, and that's not enough for a proper test.

I've used the solution in the past 12 months.

The solution is very secure. Occasionally we might get a bug or to, however, this typically happens outside of the solution. If you are within the scope of Azure, it functions very well and there is good support from Microsoft if you run into issues. Basically, there are no major bugs or glitches to contend with, unless you have a design flaw. It works quite well.

Scaling is okay, so long as the cluster isn't overloaded.

Microsoft offers excellent technical support. They are very helpful and supportive. We are quite satisfied with the level of service they provide.

The initial setup is very fast. You can set it up and just start using it. In that sense, it's great. A company shouldn't have any issues with getting it up and running.

The product does have a free trial offer, however, it is much too short. It's only 14 days and that's not enough time to run a proper POC.

Overall, we've been quite happy with the product. I would rate it at a nine out of ten.

This solution is connected with our Microsoft license. We use the E3 license, which also includes the software and analytics. We bought it and we are trying to get the best we can from the software. For now, we have some analytics, what's happening, and one of our guys looks at it and prepares reports or maybe requests some additional interventions.

It is mostly for analysis so that when something happens we can analyze it and do something about it.

Our biggest pain point is our housekeeping. We have lot of systems, we must do a lot of configurations and, because of the number of systems is not a simple part of the job, we may forget something. This solution is something which reminds us of what should we do, what stuff is most critical, what we should work on.

We would like to have something that includes the desktops, and perhaps the main system, so we can protect our systems before a threat happens.

My experience with it is that it is stable. The stability is okay and we are satisfied with it.

Our system has approximately 700 servers and a lot of other components and we are satisfied with it at this level.

We went with it because we had it. We had the opportunity to use it and started to do so. Before that, we had several areas where we wanted to improve our status. This is a tool we can check with from Microsoft and, for now, it is good for us.

The most important criteria when selecting a vendor is that the product is user-friendly. A lot of tools that are rated highly in industry reviews have complicated configurations. We would like something which is friendly enough for its administrator to use, to configure. Another criterion is price. We look for solutions we can afford. We read a lot of free white papers and try to find partners that have favorable write-ups. We use the software and do a phone conference where we can discuss further questions which are important to us.

Our firm works in the former Yugoslavia: Croatia, Bosnia, and Serbia, Macedonia, and Montenegro. The prices are different in these markets. Of course, we always want to have the lowest price but that doesn't mean price is the main factor when buying something. It is one of them, for sure. Maybe we cannot afford the most sophisticated tool there is, so price is important, but not the most important thing when we buy something.

We use Microsoft Analytics in the security department to try to raise the level of security analysis and security in general. We have implemented a lot of tools. We also have Nexpose for threat analytics, among others. Microsoft Analytics is just one of them and we implemented it because we already had it.

We tried to buy something for the desktop level. We also have systems like anti-virus, anti-malware, but these are all systems which only partly cover the threats which are now mainstream. So we would like to have a tool we can use that is faster, maybe with predictive analytics.

We have a mixed system. We also use IBM AIX in addition to the Microsoft platform. That means that ATP works successfully with Microsoft for us. If you want to implement it, the benefits are greater if you have a Microsoft in the surrounding systems.

Implementation is always easy. The hard part is then when you must have to use it, when you must work with it. An important part is education, this shouldn't be neglected.

I would rate Azure Analytics at eight out of 10. It gives us what we need, we are satisfied with the results.

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.