Azure Stream Analytics Reviews

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.

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 are using the solution to build the model. We can create multiple models like the training data. It is a new user-friendly network. You can select the data from the UI page, which is more comfortable than programming. You can process the data within half an hour and find the best model.

We received millions of records for one project. We used Kafka to get the data into our application and then processed it through Azure, whatever data was injected. We wanted to process it and build the dashboard.

The data is handled manually. For example, if you were to do the same thing with Python, you'd have to check, rebuild, and deploy the entire thing. We should be able to change the data on the fly. We can make changes and immediately see the results appear on the screen. The best part is that you'd be able to convey to stakeholders who may need to be more technically proficient. By using the dashboard, you can convince your stakeholders.

Azure Stream Analytics is more user-friendly than AWS. With AWS, there are many components to manage, requiring strong technical skills for cloud usage. Suppose you have explicit domain knowledge and understand your use case. In that case, you should be able to use the product effectively. It's the real-time data streaming feature. You configure it, and it processes coming data. There are some use cases where you want to perform calculations in real time, like edge computing or when you need to make decisions based on the incoming real-time data.

Some features require logical thinking. For example, if you want to write an integrative custom script, then it will be more convenient. Automation is available.

I have been using Azure Stream Analytics for three months.

The product is 24/7 stable.

I rate the solution’s stability a nine out of ten.

The solution is scalable. It's reliable.

I rate the solution's scalability a six out of ten.

You can communicate via email, or someone will contact you. Sometimes, it might get delayed, but the support is good.

Positive

The initial setup is easy. It has more advanced structuring. For example, if your application runs on-premises, we have tools to migrate some applications to the cloud. If the maximum complexity of the desktop use case is very high, we have to consider various factors. We might estimate that within a week, we could complete the migration. Still, we also need to thoroughly check all scenarios to ensure they function correctly and whether they impact the user's experience. This thorough examination might extend the timeline to about one month. If the use case involves data migration and the application is already built to be cloud-compatible, then the process will take a little time. One to two weeks could be more than sufficient.

If the application is tiny, then even one person is more than enough.

I rate the initial setup a nine-point five out of ten, where one is complex and ten is easy.

The product is expensive but has stability and user-centric features. Those who seek comfort, regardless of cost, will choose Azure.

Some of our project customers are returning to us and mentioning AWS-related issues. It costs them more because whatever operations they conduct on AWS incur perpetual costs. Consequently, they opt for on-premises solutions. Therefore, people may revert to on-premises infrastructure if it is costly. Otherwise, most individuals prefer cloud-based solutions. Cloud computing is generally considered superior.

I recommend Azure Stream Analytics for handling large volumes of stable and huge data. Microsoft Stream integration adds significant value, making it a comprehensive solution. Azure Stream Analytics offers necessary features without unnecessary expenses for small organisations where budget is a concern.

Overall, I rate the solution a nine out of ten.

We use Azure Stream Analytics for simulation and internal activities.

The solution’s customer support could be improved.

I have been using Azure Stream Analytics for more than two years.

Azure Stream Analytics is a stable solution.

I rate Azure Stream Analytics a ten out of ten for scalability.

I rate Azure Stream Analytics a nine out of ten for its ease of initial setup.

Azure Stream Analytics is a little bit expensive.

Overall, I rate Azure Stream Analytics ten out of ten.

We use the solution for real-time data and machine learning features.

The solution helps visualize and connect with Azure Data Lake Storage to gather information and generate alerts. Also, it helps us with pre-analytic processes to collect information from external sources.

The solution's most valuable feature is the machine learning functionality. It provides the capability to streamline multiple output components.

The solution's query languages must be more comprehensive. Also, its features for event imports and architecture need enhancement.

I have been using the solution for five years.

It is a stable solution. Although, sometimes, we encounter downtime issues.

The response time of the solution's technical support team depends on the criticality of the issue and the SLA subscription plan.

Positive

Databricks works with Python providing more capabilities and flexibilities than Azure Stream Analytics.

The solution's initial setup is straightforward when configuring inputs and outputs.

I advise others to understand the solutions' functionalities by obtaining certifications like Azure AC-400 or AC-204. It has a robust SQL language but has limitations in dealing with complex queries. I advise them to use more comprehensive solutions like Oracle or Kaspersky.

I rate the solution a nine 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.