Azure Stream Analytics Reviews

It's used primarily for data and mining - everything from the telemetry data side of things.

It's great for streaming and makes everything easy to handle. The streaming from the IoT hub and the messaging are aspects I like a lot.

It is easy to set up.

The solution is stable and reliable. 

It's a product that can scale. 

I haven't come across missing items. It does what I need it to do.

The pricing is a little bit high.

The UI should be a little bit better from a usability perspective. The endpoint, if you are outsourcing to a third party, should have easier APIs. I'd like to have more destination sources available to us. 

I've been using the solution for one year. 

The solution has been stable. There are no bugs or glitches and it doesn't crash or freeze. It's reliable. 

We have around 50 people using the solution currently.

The solution can scale well. It's not a problem at all. 

Mainly the users are developers, DevOps, and the QA automation team.

They have excellent support. They're always helpful and always resolve issues for us.

We are also using Databricks.

Compared to Databricks, Azure Stream Analytics is clear and the stream management and the queue management of the stream, the enrichment of the data analytics capability of the stream, these features are very good.

The Databricks user interface and the programming and control are better than the Azure Stream Analytics. I need to make a lot of configurations here. The control and the Azure database is a totally different service in and of itself. It is built on Sparx and Huawei, and the programming languages, and writing the jobs are better than Stream Analytics. 

The initial setup is very simple and straightforward. I would rate it a five out of five. We didn't have any trouble with it. 

We handled the initial setup ourselves. We did not have any issues that would require any third-party assistance. 

We've seen around a 10% ROI.

We find the pricing to be a little bit higher side. If that could become a bit more competitive with, for example, AWS or something, that would be great.

We pay approximately $500,000 a year. It's approximately $10,000 a year per license. 

I'd rate it a three out of five in terms of affordability.

I'd rate the solution a seven out of ten.

I like all the connected ecosystems of Microsoft, it is really good with other BI tools that are easy to connect. The stream analytics are good and it is a dependable platform.

I would like to have a contact individual at Microsoft and the price is high.

I have used Azure Stream Analytics for the past year and a half.

Azure Stream Analytics is very scalable.

When it comes to technical support it is currently reaching out to community forums. I still have not seen any kind of contact person that we can call or schedule meetings with and then just ask questions. I guess that is fine because there would be millions of people trying to reach out for calls and schedule meetings. The community forum is all right. It is helpful enough. I have been using a lot of other Microsoft tools like Power BI, but I have had questions and I have put them up on the community forums and I have received good replies.

Neutral

The initial setup was straightforward. The documentation is really helpful. It would be nice to have a support system in terms of contacts from Microsoft.

The current price is substantial.

It is a good enough choice because it is already on an established platform. The stability is very high. If there is a plan for scaling up, then it is a really good solution. I think scaling up, is one of the best items being offered. However, you need to keep in mind the costs of this robust platform. I would rate Azure Stream Analytics an eight out of ten.

We use this solution to access vulnerabilities and realtime issues that we have in Windows and Linux and for diagnostic analyzation we wanted to do for our middleware product. We have multiple data ingestion types. 

Our team also analyze the data using data visualization tools like Power BI and Tableau. Our main task is to get diagnostic metrics from the products that we are using. Based on the metrics, we send an alert if any product has run out of space. For audit purposes, we create dashboards for management and compliance.

The integrations for this solution are easy to use and there is flexibility in integrating the tool with Azure Stream Analytics.

We would like to have centralized platform altogether since we have different kind of options for data ingestion. Sometimes it gets difficult to manage different platforms. 

I have been using this solution for two years. 

This is a stable solution. 

This is a scalable solution. 

They have detailed help documentation which is very useful. When we have contacted support, they have responded in a timely manner. 

I would rate them a four and a half out of five. 

I have also worked with Apache Airflow. We choose to use Azure Stream Analytics because we wanted to access the realtime compliancy of our product. 

If your application crashes, you can incur a data loss. We wanted to check proactively so that our system is maintained and fixed soon after that crash happens. We wanted to assess the utilization of the system and create dashboards in order to visualize those compliancy checks.

The initial setup is straightforward. It takes one or two minutes. 

There are different tiers based on retention policies. There are four tiers. The pricing varies based on steaming units and tiers. The standard pricing is $10/hour. 

In order to use this solution, one should have a proper understanding of Azure fundamentals and what kind of different data storage solutions they provide. In order to have the solution working correctly, you need data ingestion, data delivery and a data destination. 

It has features and functionality to integrate with all the tools that are available in the market, not only Azure solutions.

I would rate this solution a nine out of ten.

Our primary use case involves using the app centre to retrieve lifeline data. However, the lifeline is not retrievable from the app centre due to recent changes, so we get it from Azure.

The life cycle, report management and crash management features are great. 

The product could be improved by providing more detailed analytics. For example, a graph to identify the past, present and current users. Additionally, UI and UX testing could be supported on this solution.

We have been using this solution for approximately 3 years on-premises.

The solution is stable.

The solution is scalable. We can add users when needed. Over 50 employees, including developers and QA managers use this product in our company.

We do not have any experience with customer service and support.

I cannot comment on the initial setup process because my manager set it up.

I am unsure of what the licensing costs are for this solution.

I rate this solution a six out of ten because we do not use it very often. I believe this solution has a good user interface but the solution could be improved by providing better graphics and including support for UI and UX testing.

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.

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.

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. 

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

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.

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

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. 

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. 

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. 

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. 

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.

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. 

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

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.

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.

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.

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.

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. 

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.

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.

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. 

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.

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.

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.

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.

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

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.

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.

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.

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.

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.

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.

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.

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.

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.