Azure Stream Analytics Reviews

We are using Azure Stream Analytics for small to medium size streaming datasets where you would like to flag patterns from the stream. It works well or pairs well with IoT edge scenario use cases that are on Azure. If you have exceptional conditions, such as a sensor being way off the average for the last one to five hours, then you can flag a scenario. It works well with the IoT infrastructure that Azure provides.

We didn't end up using Azure Stream Analytics in production, or for a client, we implemented it. However, Azure Stream Analytics is something that you can use to test out streaming scenarios very quickly in the general sense and it is useful for IoT scenarios. If I was to do a project with IoT and I needed a streaming solution, Azure Stream Analytics would be a top choice.

The most valuable features of Azure Stream Analytics are the ease of provisioning and the interface is not terribly complex.

Azure Stream Analytics could improve by having clearer metrics as to the scale, more metrics around the data set size that is flowing through it, and performance tuning recommendations.

I have been using Azure Stream Analytics for approximately three months.

Azure Stream Analytics is stable.

Azure Stream Analytics can improve the scaling and the connectivity to external datasets.

We are not using this solution extensively and we do not plan to increase usage.

The level of support quality depends on how much you purchased.

I rate the support from Azure Stream Analytics a four out of five.

The initial setup of Azure Stream Analytics was straightforward. It has a quick startup time and is easy to start.

I did the implementation of Azure Stream Analytics for my client. We have the developer setting the solution up and once it's in production, your infrastructure team can monitor it just like any other solution. Since it's Azure, it has a lot of metrics that allow you to be proactive to flag an issue if there is one.

I have seen a return on investment with Azure Stream Analytics. If you're not doing terribly complex scenarios, this is a quick and fast way to have your streaming pipeline set up. You won't have to invest a lot into its deployment because it's the cloud. You are not paying any upfront capital.

I rate the price of Azure Stream Analytics a four out of five.

I have evaluated other solutions, such as Databricks

Azure Stream Analytics it's good for proofs of concept and for scenarios that are not too complex. It's promising in the future, but if you start to scale out, you might want to consider other scaling solutions, such as Databricks.

Got it. And do you see a return on investment with this one?

I rate Azure Stream Analytics a nine 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 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 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.

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.

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. 

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.

We mainly use this product for real-time data injection and reporting purposes.

We find the query editor feature of this solution extremely valuable for our business.

Also, the multiple window types that are available in this solution, are extremely helpful.

The solution doesn't handle large data packets very efficiently, which could be improved upon.

We would also like more variation in the output types that this solution can produce.

We have been using this solution for three or four years.

The stability of this solution was problematic initially, but it has improved as later versions have been released, and is now very good.

We have found this product to be easily scalable.

The initial setup of this solution is very easy, and the deployment takes place instantly.

The licensing for this product is payable on a 'pay as you go' basis.  This means that the cost is only based on data volume, and the frequency that the solution is used.

This solution is easy to use and has good fault tolerance, so I would recommend it to other organizations.

I would rate this solution an eight out of ten.