Azure Stream Analytics Reviews

We use the solution to analyze application logs. It helps review incidents and production issues.

The solution's most valuable feature is its ability to create a query using SQ.

The solution's interface could be simpler to understand for non-technical people. Also, the chart feature could be more user-friendly. Presenting or elaborating on an incident to management or executives from other non-technical departments becomes challenging. We have to create another graph and include it in the presentation slides.

We have been using the solution for two years.

The solution is stable.

The solution is scalable. We have around more than 100 developers using it.

We follow virtual documentation in case of technical errors for the solution.

If you have Azure DevOps or are using the Azure ecosystem, you must go for the solution. I rate it an eight out of ten.

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.

I used it once for a project demo to a customer for an IoT solution. In this demo, the data was collected from the sensors, and it was sent to Power BI reports. The collected data was analyzed by using the analytics tools to get some insights.

This project was the first project for our company to start the development of IoT solutions. We have only used it for a demo, and we have kept it for demo for other customers. If any customer wants to deploy it, we would use it in production.

Real-time analytics is the most valuable feature of this solution. I can send the collected data to Power BI in real time.

It is not complex, but it requires some development skills. When the data is sent from Azure Stream Analytics to Power BI, I don't have the access to modify the data. I can't customize or edit the data or do some queries. All queries need to be done in the Azure Stream Analytics. 

I have used this solution for a few months.

I don't know about its stability because we didn't use it in production. We only used it for testing.

Its scalability is okay. In Azure Stream Analytics, I can add more data sources through reference or IoT hub. 

For the demo, we had a team of 20 users. The customer was looking at allowing around 20,000 users for this solution.

I contacted their technical support once because I found an issue with Azure Stream Analytics. The technical support engineer was very supportive.

The initial setup was straightforward for me. I read some articles on the Internet, and it worked fine for me. It took us one to two weeks to deploy it.

If you want to deploy IoT services, this solution will be very helpful for real-time applications and for collecting data.

I would rate Azure Stream Analytics a seven out of ten. 

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.

The company I'm working for is basically one of the biggest companies in the entire Gulf region, including Dubai, Qatar, and Oman. Our core domain is providing logistics. They have different warehouses across the country, and we use it to track the movement of forklifts and people working at the warehouses. 

The main thing we are focusing on right now is accident avoidance. For example, one forklift is coming through one aisle, and another person is trying to enter the same aisle. We provide a solution that can track the person and forklift in real-time. 

We're also using it to solve many business problems one by one. Stream Analytics plays a major role in streaming all these huge datasets because we have warehouses spread across the country. It's able to handle millions and millions of events in a few seconds.

If we're not using Stream Analytics, how can we track the real-time? From the end user's perspective, Stream Analytics forms the main backbone for the entire pipeline and all the technologies.

The company can see the real-time location and track everything, just because of Stream Analytics. Without Azure Stream Analytics, we can't do any real-time tracking. We can use other messaging systems like SQL, but when it comes to scaling, collecting, getting a lot of events, recalling it, find out where it's used, Stream Analytics is better. You might have to collect from millions and millions of services and devices and beacons. All of that would be pushing the data into the Stream Analytics. 

I like the IoT part. We have mostly used Azure Stream Analytics services for it. This is the most valuable part because this is using the streaming service. It's valuable because there's no other way for us to handle it. 

It has the support of Azure storage, long storage, and access data. It has support from the SQL server. Azure also supports added access to data because we need the data from static data and dynamic data to represent them, and that's not going to change very frequently.

When we are getting the warehouse's location through this stream analytics, we have to merge some information from our static database, and finally, we have to show it all within a dashboard or something on the map.

Without the streaming analytics part, I don't think it's possible to handle it. We can use some other messaging system, but we might have some scaling issues and among others too. I know that Stream Analytics is fantastic in that we don't have to worry about any other activities. We can further scale it too. We can go for the upgraded service if needed, based on our traffic and the number of data we have been receiving.

Natively, I found it beneficial, and the integration was smooth. We're already using some other Microsoft technology packs, so it's easy to integrate them all.

As the Stream input and output enables very smooth integration with other cloud services, for example, Azure Cloud Concepts, or Cosmos DB, with minimum coding, and with the minimum level of queries, we can directly output all these outputs and push the normal data for historical data storage.

The collection and analysis of historical data could be better. We use historical data and an assimilating algorithm to give us insights into the entire business process. 

We can collect all the historical data periodically to get insights into current business trends. For example, which area is getting emptied most of the time or which area is getting underutilized, and so on. 

I've been working with Azure Stream Analytics for about two years.

We don't have to worry about scalability. It's in the cloud and can have millions and millions of things connected. The software part is easy to scale. You just have to add all the hardware. For the web application, the hosting part can be scaled. We don't have to worry about the desktop as the solution is deployed in the cloud. The scalability is based on our choices. It's not like it's manually hosted in private, and we have to scale it vertically.

Our infrastructure team has the flexibility to call the Microsoft guys to look into the matter if there is something wrong on their part.

The initial setup was very complex because of the hardware. We had to spend almost an entire day just to put the hardware part in the right places, following some best practices. 

It took us more than one and a half years, and we're still left with some deployments to do. We initially tested it in a few small areas, and then we expanded it to cover the entire area.

I found it a little challenging, we struggled, and we did it. We're still doing a lot of stuff for the elite features and other deployments. We follow the deployment strategy, and it's almost automated. We're trying to add a few features and deploying them. The final stage of deployment is where the rest of the entire process is done through continuous integration.

It requires maintenance in terms of hardware and the software part. I don't think any solution is totally bug-free. We generate service requests all the time, and they are fixing it.

The IoT hardware requires more maintenance because we know we have limited battery life, and we have to check all the devices. We need to keep checking those things, and we have automated that. But it still needs to be manually reconnected to the battery.

We have a team of people supporting this project. We have about ten members, some of whom were core developers. Four or five developers developed the cloud part. Two hardware engineers were responsible for all these deployments in the warehouse.

I would advise potential users to properly plan and structure their static data and the reference data before putting it into the Stream Analytics.  

On a scale from one to ten, I would give Azure Stream Analytics an eight.

Our primary use case is mainly to ingest real time data streams into permanent storage places like databases, block storage, etc.

The biggest improvement for us has been that it now takes much less time for us to receive valuable information. Basically, as soon as it appears in our real time data source, in a matter of seconds, it is already in our database.

The value of this solution is the deep integration it provides with other Azure resources which we use a lot. Our whole infrastructure is pretty much based on Azure so ease of integration is a valuable feature. Secondly, the simplicity of the solution is great. You don't need to set up much, you just make a selection, select a destination, and you're off. 

There are some improvements that could be made, first of all in pricing, because right now the pricing is a bit unclear. It's hard to estimate how much of that is a local issue but you can't figure out how prices are calculated or the proprietary part of the cost. Another area that could be improved is that if something does go wrong, it's very hard to investigate what caused it and why. The logging is available but it lacks detail and doesn't provide much information.

I've been using this solution for two years. 

The solution has an acceptable level of stability although, as mentioned, if it does fail, it's pretty difficult to find out the cause. 

It's very easy to scale this solution. We probably have a couple of hundred users and we have developers who deal with maintenance. This is our main tool for real time data streaming. 

The initial setup is quite straightforward. Because of the good integration, you select your real time data, store the destination where you want to write it and you probably don't even need to transform with data. You basically create a mapping descent source. We had a proof of concept in place, so I would say deployment took two working days without having a deployment plan. 

We have a good ROI because we are able to deliver solutions very quickly and customers are happy with that. 

We evaluated and carried out a comparison with Oracle. The results were pretty much the same for both in terms of real time data streams, but were very much tied to their own cloud solutions. If you work with Oracle i'ts probably best to go with Amazon.

My simple advice would be to not scale up initially. Also, if you have questions don't just rely on the official documentation, but use other resources such as a blog by a developer, because sometimes that can be more helpful than documentation provided by the company.

The best advice I can offer would be that if there is a simple solution available, do not try to complicate things. 

I would rate this solution an eight out of 10.