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Azure Stream Analytics OverviewUNIXBusinessApplication

Azure Stream Analytics is the #5 ranked solution in our list of Streaming Analytics tools. It is most often compared to Databricks: Azure Stream Analytics vs Databricks

What is Azure Stream Analytics?
AzureStream Analytics is a fully managed event-processing engine that lets you set up real-time analytic computations on streaming data.The data can come from devices, sensors, web sites, social media feeds, applications, infrastructure systems, and more.

Azure Stream Analytics is also known as ASA.

Azure Stream Analytics Buyer's Guide

Download the Azure Stream Analytics Buyer's Guide including reviews and more. Updated: October 2021

Azure Stream Analytics Customers
Rockwell Automation, Milliman, Honeywell Building Solutions, Arcoflex Automation Solutions, Real Madrid C.F., Aerocrine, Ziosk, Tacoma Public Schools, P97 Networks
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Pricing Advice

What users are saying about Azure Stream Analytics pricing:
  • "The cost of this solution is less than competitors such as Amazon or Google Cloud."

Azure Stream Analytics Reviews

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SK
Consultant
Real User
A serverless scalable event processing engine with a valuable IoT feature

Pros and Cons

  • "I like the IoT part. We have mostly used Azure Stream Analytics services for it"
  • "The collection and analysis of historical data could be better."

What is our primary use case?

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.

How has it helped my organization?

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. 

What is most valuable?

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.

What needs improvement?

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. 

For how long have I used the solution?

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

What do I think about the scalability of the solution?

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.

How are customer service and technical support?

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

How was the initial setup?

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.

What about the implementation team?

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.

What other advice do I have?

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.

Which deployment model are you using for this solution?

Public Cloud

If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?

Microsoft Azure
Disclosure: My company has a business relationship with this vendor other than being a customer: Partner
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SR
Associate Principal Analyst at a computer software company with 10,001+ employees
Real User
Top 20
Helpful technical support and relatively easy to set up but is not cloud agnostic

Pros and Cons

  • "Technical support is pretty helpful."
  • "Early in the process, we had some issues with stability."

What is our primary use case?

We were doing some level of stream data processing, so we had some use cases which were related to IoT. We had some IoT devices getting data in from other IoT devices and Azure Streaming Analytics has a special streaming analytics offering for IoT devices. Basically it was used for that. 

What is most valuable?

I basically use two features that are useful. One is Azure Event Hubs, and that is used in conjunction with Azure Streaming Analytics. One is the broker and one is the processing engine. With the processing engine, the SQL way of dealing with things, with streams, is what I like, compared to other solutions, which are more like Scala or Spark-based, where you need to know the language. This was comparatively easy to use with its ability to write SQL on streams.

Technical support is pretty helpful. 

It's my understanding that the setup is pretty straightforward.

What needs improvement?

With Azure specifically, the drawback is it is a very Azure-specific product. You can't connect it to external things out of Azure. For example, Spark or Databricks can be used in any cloud and can be used in AWS. This product doesn't work that way and is very Azure-specific. It's not a hybrid solution and it's not a cloud-agnostic solution, where you put it on other clouds, et cetera. 

We had some connections which we wanted to make with AWS, which we couldn't do with this. We had to use something else for that.

Early in the process, we had some issues with stability.

You cannot do joins on streams of data. For example, one stream joining with another stream. Real-time to real-time joins, you're not able to do that. You can only join your stream with static data from your Azure storage. 

For how long have I used the solution?

I've used the solution for one and a half to two years.

What do I think about the stability of the solution?

There were some issues with the IoT jobs when streaming Azure Streaming Analytics, which are high proof now. That said, earlier, we used to have a lot of issues with the erratic behavior of jobs. If data is not in the way they expect it, if they are not modeled correctly, then the jobs tend to break or fail quite a lot. That was one issue we had.

How are customer service and technical support?

We've been in touch with technical support. There was a time when jobs failed a lot and we couldn't understand the reason. When we talked to the spec tech support, they've looked into our data and told us that it's not exactly modeled as how Azure Stream Analytics needs it. That wasn't very clear when we got it. 

They were helpful. There were issues which they handled, which they told us about. The communication was great.

We had the support package included.

Which solution did I use previously and why did I switch?

I'm now an analyst, so I don't use the products per se, however, prior to this, I have used Azure Streaming Analytics quite a lot. Currently, I'm working a bit on Databricks Spark Streaming. These two are, I would say, what I have used personally.

How was the initial setup?

The product was set up before I started out, however, what I can say, having set up some things personally, is it is comparatively straightforward and the Microsoft support on that is comparatively good.

What's my experience with pricing, setup cost, and licensing?

In terms of pricing, you can't compare it to open source solutions. It would be higher compared to open source, of course, however, with the support and everything you're getting, I would say the price, in general, is fair. 

I have seen AWS as well and can compare it to that and I would say it is fair. The problem is it is not exactly dynamic or serverless, with how the way things are in the cloud. Therefore, it is not completely utilized. You have to set up things beforehand with some level of capability and capacity beforehand. In regards to the price, it's not too high and also not too low.

Their pricing is not exactly serverless. It's per hour. A lot of others are moving towards pricing based on the amount of data you pull. Streaming Analytics charges per hour, and in that sense, you need to set up the capacity by trial and error, literally. 

Which other solutions did I evaluate?

I'm comparing the Azure Stream Analytics, AWS Kinesis, GCP Pub/Sub, and Dataflow. So I'm currently in the process of writing that research.

What other advice do I have?

If you are in the Azure world completely, and you're using the Microsoft stack completely, and you do not have the need to go in any other cloud, then it makes sense to use this solution as it integrates very well within the Azure ecosystem. 

For IoT use cases, if you want to do real-time dashboarding with Power BI, it's great. Those kinds of things are where it has its niche. However, if you want a cloud-agnostic kind of solution, where you do not want to be stuck with just Microsoft, then there are other solutions out there such as Confluent, Kafka, Spark Streaming with Databricks, et cetera. You'll get the flexibility you need using any of those platforms.

I'd rate the solution at a seven out of ten. We had some issues with the jobs not behaving properly. They promise a lot, however, sometimes that doesn't happen and we realized that later. Some things under the hood, we couldn't really understand and we needed to get in touch with support. Those kinds of issues are where I would say it needs a bit of improvement, and maybe that's why I cut off two or three points.

Which deployment model are you using for this solution?

Public Cloud
Disclosure: I am a real user, and this review is based on my own experience and opinions.
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Learn what your peers think about Azure Stream Analytics. Get advice and tips from experienced pros sharing their opinions. Updated: October 2021.
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Alwin George Daniel
RPA DevOps Engineer at SG Analytics
Real User
Top 20
Effective Blob storage and the IoT hub save us a lot of time, and the support is helpful

Pros and Cons

  • "The most valuable features are the IoT hub and the Blob storage."
  • "There may be some issues when connecting with Microsoft Power BI because we are providing the input and output commands, and there's a chance of it being delayed while connecting."

What is our primary use case?

We have different kinds of IoT devices placed in different countries including the UK, US, and others. They are configured with our IoT hub and we get the logs from them accordingly.  We have these logs connected with the Stream Analytics suites and Microsoft Power BI. Whatever updates and other activity is happening on the devices are streamed into Azure and Power BI so that we can see them.

If we find any error messages then we have to check the health of the corresponding IoT devices, databases, and configuration.

How has it helped my organization?

This gives us a real-time monitoring system that we can use to analyze the health of our IoT devices. Previously, when something was not working properly then we would receive messages in our email using the TeamWork application. Now, instead of checking email, we receive an alert ping that we can hear, which allows us to evaluate how well the machine is doing. We can check the performance and other relevant metrics.

In general, it gives us more visibility in terms of what is going on. We used to receive between 10,000 and 20,000 emails per week, which was hectic for us to calculate and keep track of. Since implementing Azure, we have been able to monitor things very easily. Not only does it create an interval for the logs but it reduces the number of duplicates.

We have not eliminated the messages that come in as email, as high-priority messages are still delivered in that manner. For example, if there is a power shut-down then we will be notified via email. This is set up in case we miss these types of messages in the BI platform.

What is most valuable?

The most valuable features are the IoT hub and the Blob storage. All of the logs and other data that we are getting can be stored in Blobs.

The interface is user-friendly.

What needs improvement?

There may be some issues when connecting with Microsoft Power BI because we are providing the input and output commands, and there's a chance of it being delayed while connecting.

For how long have I used the solution?

I have been using Azure System Analytics for just more than one year.

What do I think about the stability of the solution?

This product is stable but if our VM goes down then we are not able to get a proper instance update. When this happens, we need to kill these instances. Situations like this only happen rarely.

What do I think about the scalability of the solution?

The scalability is based on the requirements. If the requirements are high then highly-scalable machines are needed. If it is more manageable then it is cheaper. I think that scaling is really about the cost.

We have a development team and an operations team that is working with Azure Steam Analytics. There are seven or eight people in the operations team. The customer also has access to the platform if they require it.

How are customer service and technical support?

If you raise a ticket with technical support then they will contact you within 24 hours. However, we have not faced many issues, so we haven't had much involvement with them.

There is a diagnostic tool available in Azure and you can check to see if you have any issues on your end. If there are problems then you can contact support for assistance.

Overall, I think that the support is very helpful.

Which solution did I use previously and why did I switch?

Since transitioning from our email-only solution, we have been able to set the interval that we use to retrieve logs from the devices.

We did not use a similar product before this one for the same purpose. The company has been using Azure since before I joined, although they had used AWS for other tasks. At this company, I have not had the opportunity to work on AWS.

How was the initial setup?

I have not completed a deployment for production purposes. Rather, I have performed a setup for training with Azure and an IoT simulator. In this case, we just check the logs during my practice session. My role in the operation was to lead the management team.

The training deployment that I completed was user-friendly and anyone can easily do it. Even as part of the operations team, I was able to capture the details and complete the deployment really quickly.

The only difficulty that I faced was connecting with the different machines in the outside layer, such as BI or Kibana. Depending on the application I was connecting with, there were issues with it.

What about the implementation team?

The deployment was done by our development team, and they are responsible for the maintenance as well. Because it is a platform as a service, Azure takes care of almost everything.

What was our ROI?

I am not familiar with the details of the investment. This is something that is handled completely by the product owner. This would be my manager or the Delivery Manager.

What's my experience with pricing, setup cost, and licensing?

The cost of this solution is less than competitors such as Amazon or Google Cloud. If we only use one hour then we are only charged for one hour. It is very easy and some products are more expensive.

What other advice do I have?

Azure Stream Analytics is something that we were able to easily learn. It doesn't take much programming sill, so I feel that it is easy to start using.

Other than the problem with delays in connecting to Microsoft BI, Kibana, or other monitoring tools, I don't have any other issues with this product.

I would rate this solution a nine out of ten.

Which deployment model are you using for this solution?

Public Cloud

If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?

Microsoft Azure
Disclosure: I am a real user, and this review is based on my own experience and opinions.
RD
Senior IT Product Manager at a manufacturing company with 10,001+ employees
Real User
Good-looking user interface, works well with IoT applications

Pros and Cons

  • "I like the way the UI looks, and the real-time analytics service is aligned to this. That can be helpful if I have to use this on a production service."
  • "Sometimes when we connect Power BI, there is a delay or it throws up some errors, so we're not sure."

What is our primary use case?

We have a lot of Internet-of-Things stream data from various machines and IoT edge devices. We stream the data and use it for future data analytics, like machine learning or predictive analytics, so we need some dashboarding done on a Power BI report. And to do that, we have to process this vast telemetry stream data, and that's why we use Azure Stream Analytics.

What is most valuable?

I like the way the UI looks, and the real-time analytics service is aligned to this. That can be helpful if I have to use this on a production service. So this particular Azure Stream Analytics absolutely fits into that. Also, using it requires very few clicks. The UI is set up so that I don't need to spend much time on this. The way that Stream Analytics manages workloads is also good.

What needs improvement?

Sometimes when we connect Power BI, there is a delay or it throws up some errors, so we're not sure. We try to resolve it on an issue-by-issue basis. For some reason, Microsoft Power BI and Azure Stream Analytics have connection issues, but I can't say what the problem is at a product level. 

For how long have I used the solution?

I've been using Azure Stream Analytics for about a year and a half.

What do I think about the stability of the solution?

At this point, it's too early to say whether Stream Analytics is stable. I'm not sure how stable it will be going forward. So far, I haven't had any issues.

How are customer service and support?

We have internal Azure tech support that takes care of this, so we haven't had a need to go outside and contact Microsoft.

How was the initial setup?

The documentation is clear, so we could follow it and set Stream Analytics up. In some of the new areas of the software, you require some hands-on help and some support, but for any internal deployment, we have an Azure support team. They help us with that, so it's not a challenge. 

What's my experience with pricing, setup cost, and licensing?

From a pricing perspective, I don't have a clear understanding of how the streaming units are built. It takes me a long time when a price report comes in at the end of the day. I spend a lot of time on this. The way the reports break down the charges is very confusing. So that is something that has to be improved. There is a clear, concise reporting structure for other Azure services — the full subscription, Azure Public Cloud service, etc.—but the streaming unit billing part of Stream Analytics confuses me all the time.

Nevertheless, the features are decent, so I continue to use it. Still, it takes me a lot of time when I have to approve specific invoices at the end of the month. But overall, the price is fair because it charges per streaming unit. The price is reasonable. It is what you would expect, given the kinds of features it has. 

What other advice do I have?

I rate Azure Stream Analytics eight out of 10. It's hard to fully evaluate a product after just one and a half years, so from that perspective, I say 8. Everything has some room for improvement. If my manager asks me to rate myself, I probably wouldn't say a perfect 10. If you tell me that Stream Analytics is done and there won't be any updates in the future then I can let you know if it's a 10. This is a great product that works better than most of its competitors. If someone is using Event Hub, they should go with Azure Stream Analytics instead. Stream Analytics is more cost-effective than AWS Kinesis. Both are excellent products, but I am more comfortable with this in terms of the features and the improved ROI. And if you're working IoT, this is the solution to use. 

Which deployment model are you using for this solution?

Public Cloud

If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?

Microsoft Azure
Disclosure: My company has a business relationship with this vendor other than being a customer: Partner
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AF
BI Developer at a tech services company with 51-200 employees
Real User
Great integration with other Azure resources, is simple, and has been a great time-saver for us

Pros and Cons

  • "Provides deep integration with other Azure resources."
  • "If something goes wrong, it's very hard to investigate what caused it and why."

What is our primary use case?

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

How has it helped my organization?

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.

What is most valuable?

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. 

What needs improvement?

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.

For how long have I used the solution?

I've been using this solution for two years. 

What do I think about the stability of the solution?

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

What do I think about the scalability of the solution?

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. 

How was the initial setup?

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. 

What was our ROI?

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

Which other solutions did I evaluate?

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.

What other advice do I have?

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. 

Which deployment model are you using for this solution?

Public Cloud
Disclosure: I am a real user, and this review is based on my own experience and opinions.
TG
Business Architect Expert at a government with 51-200 employees
Real User
Stable, with a quick setup and lots of functionality

Pros and Cons

  • "The solution has a lot of functionality that can be pushed out to companies."
  • "The solution offers a free trial, however, it is too short."

What is most valuable?

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.

What needs improvement?

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.

For how long have I used the solution?

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

What do I think about the stability of the solution?

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.

What do I think about the scalability of the solution?

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

How are customer service and technical support?

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

How was the initial setup?

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.

What's my experience with pricing, setup cost, and licensing?

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.

What other advice do I have?

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

Disclosure: I am a real user, and this review is based on my own experience and opinions.
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Yasmina Ali
Collaboration Consultant at a tech services company with 201-500 employees
Consultant
Top 20
It is good for real-time analytics, but requires some development skills

Pros and Cons

  • "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."

What is our primary use case?

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.

What is most valuable?

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

What needs improvement?

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. 

For how long have I used the solution?

I have used this solution for a few months.

What do I think about the stability of the solution?

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

What do I think about the scalability of the solution?

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.

How are customer service and technical support?

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

How was the initial setup?

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.

What other advice do I have?

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. 

Which deployment model are you using for this solution?

Private Cloud

If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?

Microsoft Azure
Disclosure: IT Central Station contacted the reviewer to collect the review and to validate authenticity. The reviewer was referred by the vendor, but the review is not subject to editing or approval by the vendor. The reviewer's company has a business relationship with this vendor other than being a customer: Partner