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.
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.
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.
Senior Cloud Solution Architect Advanced Analytics & A.I. at Banco de Credito
Real User
Top 20
2023-05-17T20:09:00Z
May 17, 2023
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.
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.
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.
Associate Principal Analyst at a computer software company with 10,001+ employees
Real User
2021-09-24T19:49:20Z
Sep 24, 2021
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.
I would advise potential users to properly plan and structure their static data and the reference data before putting it into the Stream Analytics. On a scale from one to ten, I would give Azure Stream Analytics an eight.
Collaboration Consultant at a tech services company with 201-500 employees
Consultant
2020-10-11T08:58:04Z
Oct 11, 2020
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.
BI Developer at a tech services company with 51-200 employees
Real User
2020-09-22T07:16:06Z
Sep 22, 2020
My simple advice would be to not scale up initially. Also, if you have questions don't just rely on the official documentation, but use other resources such as a blog by a developer, because sometimes that can be more helpful than documentation provided by the company. The best advice I can offer would be that if there is a simple solution available, do not try to complicate things. I would rate this solution an eight out of 10.
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.
Assistant Director - IT Governance Support at a insurance company with 1,001-5,000 employees
Real User
2018-08-22T11:28:00Z
Aug 22, 2018
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.
Azure Stream Analytics is a robust real-time analytics service that has been designed for critical business workloads. Users are able to build an end-to-end serverless streaming pipeline in minutes. Utilizing SQL, users are able to go from zero to production with a few clicks, all easily extensible with unique code and automatic machine learning abilities for the most advanced scenarios.
Azure Stream Analytics has the ability to analyze and accurately process exorbitant volumes of...
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.
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.
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.
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.
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.
I'm an end-user. Overall, I've been satisfied with the product. I'd rate the solution eight out of ten.
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.
I'd rate the solution a seven out of ten.
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.
Overall, we've been quite happy with the product. I would rate it at a nine out of ten.
I would advise potential users to properly plan and structure their static data and the reference data before putting it into the Stream Analytics. On a scale from one to ten, I would give Azure Stream Analytics an eight.
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.
My simple advice would be to not scale up initially. Also, if you have questions don't just rely on the official documentation, but use other resources such as a blog by a developer, because sometimes that can be more helpful than documentation provided by the company. The best advice I can offer would be that if there is a simple solution available, do not try to complicate things. I would rate this solution an eight out of 10.
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 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.