Azure Stream Analytics Reviews

We use Azure Stream Analytics to process online event streaming data. It's a versatile solution that can handle various types of streaming data, including deployed streaming data. 

It also supports JSON format and enables us to analyze IoT data from different organizations within the group.

I appreciate this solution because it leverages open-source technologies. It allows us to utilize the latest streaming solutions and it's easy to develop. 

It also provides quick access to data and allows us to see the results efficiently. Additionally, it offers a graphical view, which helps us understand the data and its transformation. I find this feature quite advanced, and I really like it.

One area that could use improvement is the handling of data validation. Currently, there is a review process, but sometimes the validation fails even before the job is executed. This results in wasted time as we have to rerun the job to identify the failure. It would be beneficial to have better error handling and early detection mechanisms in place. 

Additionally, there should be improved support for data joining and ensuring that customer matching is accurate. It's crucial to address these issues and add enhancements on top of the existing solution.

I have been using Azure Stream Analytics for six years. We use the latest version. 

Stability is good, I have not seen any issues.  However, I have encountered some issues where jobs fail due to errors. It requires capturing and addressing those issues. One challenge is that the fine-tuning of the computer resources needs to be done manually. It would be beneficial if it could be automated.

Overall, I would rate the stability an eight out of ten. 

Scalability is pretty good. It is pretty straightforward. We have over 1000 users. We have plans to increase the usage of this solution and expand at a global level. 

I haven't had the chance to use tech support because I have been working with Microsoft for over 16 years. I have access to documentation, Slack solutions, and online forums. I also have contacts with colleagues at Microsoft, so I usually find solutions through documentation and other resources.

The initial setup is really straightforward. I did it in one hour. 

The deployment process starts by getting the data and performing data preprocessing tasks such as data cleaning and enrichment. We use MLflow and MLOps practices to fine-tune the data and align it with the desired artifacts. Once the data is prepared, we generate all the necessary results. And then provide customers with a visualization of how the data will appear.

Since I leverage machine learning and create automated scripts with the help of chatGPT, I don't require a large technical staff for deployment. I only need a couple of front-end engineers. A team of seven people is sufficient for me.

Customers need to pay for a license. However, we have a three-year upfront licensing arrangement, which helps to keep the costs relatively low.

I evaluated other options. However, Azure Stream Analytics stood out and proved to be the most effective solution for me.

I would advise you that Azure Stream Analytics is highly scalable, reliable, and provides advanced features. It is straightforward to deploy, especially for users with hands-on skill sets. Additionally, the documentation is comprehensive, making it easy to understand and implement.

Overall, I would rate this solution a perfect ten. Microsoft has done an excellent job with this solution.

The product is used just for the extraction, transforming, and loading of the data to the data warehouse.

It is quite simple and straightforward. The product is very user-friendly.

We like that it's using the Azure platform. We can use the old Azure functionality also. We prefer to use the Azure environment. It's something the client uses. 

The analytics and reporting features are okay.

It's scalable as a cloud product.

The solution is stable. 

I'm not sure if there are any areas that are lacking

The initial setup is complex. It should be easier for new users who may not have too much Azure experience. 

I've only used the solution for six months. 

I find the product to be quite stable now. There are no bugs or glitches. It doesn't crash or freeze. It's reliable. 

This solution is on the cloud and, therefore, can scale quite well. It can meet a company's needs if the organization needs to expand. 

I don't recall ever having any interaction with technical support. I can't speak to how helpful or responsive they would be. 

It is not a straightforward setup. It is pretty complex. That said, you can find answers to help you set it up by Googling aspects of the product. If your team is familiar with Azure, it might be a bit easier. It they are not, they will find it difficult. 

We'd like the setup to be simpler in the future if possible. 

I'd rate the solution three out of five in terms of ease of deployment. 

The pricing is on the client's side. I can't speak to the exact cost of the product.

I'm an end-user.

Overall, I've been satisfied with the product. I'd rate the solution eight 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.

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.

Positive

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.

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. 

I use the solution in my company for real-time analytics on IoT data.

Azure Stream Analytics was not meeting our company's expectations because it was tedious to change the job, write queries, or if I needed to change something, I needed to stop the entire stream processing to change the job so that the changes could take effect. The aforementioned reasons were concerning, but I think that many of the issues related to the product have been resolved with the help of Microsoft Fabric.

The only challenge was that the streaming analytics area in Azure Stream Analytics could not meet our company's expectations, making it a component where improvements are required.

I have been using Azure Stream Analytics for two and a half years. My company has a partnership with Microsoft.

It is a stable solution.

Azure Stream Analytics is a scalable solution.

My company deals with small, medium, and enterprise-sized customers for the product.

The solution's technical support is good. As soon as Microsoft's product team gets involved with the product, the support that our company receives from Microsoft is good. I rate the technical support a ten out of ten.

Positive

I also use Microsoft Fabric.

Azure Stream Analytics is easy to deploy. Other than the streaming analytics part, the rest of the areas in the product were fine. Templates were available in the product for the deployment process. The product's deployment process and connectivity were smooth. Scaling options in the product are good.

The solution can be deployed in a couple of minutes.

The product's price is at par with the other solutions provided by the other cloud service providers in the market.

Against Azure Stream Analytics, I had considered products like Amazon Kinesis and Google Cloud Dataflow.

Azure Stream Analytics for anomaly detection was something that was not meeting our company's expectations, but the new tool within Microsoft Fabric for real-time analytics is really good for even Azure Stream Analytics as it allows me to get alerts and use data activators, so I can take instantaneous actions. Regarding anomaly detection, it is much easier and faster with the availability of an SQL database, which is a real-time database. Within Microsoft Fabric, there is a component called real-time analytics, which consists of multiple tools like Eventstream, KQL database, and data activator.

Speaking about Microsoft Fabirc's features that were valuable for processing large volumes of data in real-time, I would say that our company is able to process a terabyte of data daily in real-time. The scaling part of the is outstanding, and the connectivity between the components is smooth. For the overall experience provided by Microsoft Fabric, I rate the tool a ten on ten if I specifically consider real-time analytics. Within Azure Stream Analytics, real-time analytics was not good, but in Microsoft Fabric, it is.

The product's integration capabilities have always been good since I could integrate multiple sources and ingest data.

Though my company has a maintenance team, the product does not need to be maintained as such. It is when we receive alerts in our company that we check the product. Dedicated maintenance or support is not required for the product.

Learning to use the product is a straightforward and easy process. I find AWS to be a bit confusing compared to Azure Stream Analytics.

Compared to Azure Stream Analytics, Amazon Kinesis, and Google Cloud Dataflow, I find Microsoft Fabric to be the best.

I rate Microsoft Fabric a ten out of ten.

I rate Azure Stream Analytics as seven to seven and a half 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.