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.

Azure Stream Analytics is a simple tool used to deploy and implement.

Regarding operational efficiency, Azure Stream Analytics has improved our workload management. While it hasn't significantly impacted cost savings, it has made it easier to move from batch processing to real-time analytics, which took only a few days to implement, especially for IoT scenarios.      

The most valuable features of Azure Stream Analytics are its simplicity and low cost. It's easy to implement and maintain pipelines with minimal complexity. It is excellent because it allows real-time data updates, with changes reflected in seconds.

Azure Stream Analytics is challenging to customize because it's not very flexible. It's good for quickly setting up and implementing solutions, but for building complex data pipelines and engineering tasks, you need more flexible tools like Databricks.

I have been using Azure Stream Analytics for two years.

We encountered some bugs with Azure Stream Analytics about two years ago, which caused some instability.

The scalability is excellent; I rate it a ten. While the costs increase as we scale our workloads, the solution performs well for SQL and simple data transformations. It might not be as cost-effective for complex tasks, but for most of our needs, it works efficiently.

I haven't worked directly with Microsoft's technical support for Azure Stream Analytics. However, we did have some chats with Microsoft's engineering team about stability issues, and they were informative. Overall, I would rate their support as neutral, as we didn't interact extensively with them.

Our deployment of Azure Stream Analytics took only a few minutes using the Azure cloud user interface for initial testing. Later, we used tools like Terraform to automate workflows and deploy every needed stream pipeline. The deployment was done in-house.

The deployment was done in-house.

When scaling up, the pricing for Azure Stream Analytics can get relatively high. Considering its capabilities compared to other solutions, I would rate it a seven out of ten for cost. However, we've found ways to optimize costs using tools like Databricks for specific tasks.

If you want to start quickly and simply with low technical latency, I recommend Azure Stream Analytics. It's easy to manage, implement, and handle, but it's not the most flexible solution. Overall, I rate it an eight 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. 

We use it to stream data from IT devices and process it.

We use almost all Azure services, right from Azure AD, Event Hub, Cosmos DB, Azure Stream Analytics, Azure monitoring services, Azure ML Studio, and everything. 

The way it organizes data into tables and dashboards is very helpful, along with its data visualization capabilities.

Easier scalability and more detailed job monitoring features would be helpful.

Another room for improvement is the ingestion of data. 

I have been using it for a year now. 

In my department alone, about 510 people use it.

We have contacted customer service and support, but usually, our operation team handles that. They contact different teams depending on the issue, like storage, SQL database, or Cosmos DB teams.

So it's a collaborative effort.

The initial setup is easy, just like any other cloud service installation.

I would recommend based on a specific use case and see if it fits with Azure Stream Analytics, real-time processing, and integration services. 

For example, if your use case involves IoT devices, Azure Stream Analytics would be a good choice. If everything seems like a good fit, then I would say go ahead and use it.

Based on my experience, I would rate the solution a seven 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.

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. 

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.

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

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

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

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

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

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

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

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

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

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

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

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

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

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