Azure Stream Analytics Reviews

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 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 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.

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.

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.

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 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.