I have worked on many use cases involving IoT platforms over the last 10 years. For instance, with a large client in the U.S., I helped track vehicles for Paccar and other companies such as Amazon, URS, or UPS. They monitor many vehicles traveling across the U.S., capturing data regarding vehicles' problems or accidents. This information is processed using Kafka or Event Hubs and pushed to our platform for analytics, where report tools such as Power BI visualize data. For example, if any telemetry or real analytics data or streaming data comes from IoT, Azure provides the combination of Event Hubs and Streaming Analytics, and in open source, Kafka is used. For inter-system communication, the pub and sub model allows some services to publish data to the queue or topic in any services such as RabbitMQ, while Azure offers Service Bus. If you want to use real-time telemetry data, capturing things such as share market data or vehicles on highways, Event Hubs is recommended to pull that data. We can validate basic data schema using Azure Stream Analytics, determining if data processing should succeed or fail; if it succeeds, we process it with our platform, specifically our ADLS Gen2. For any basic validation that fails, another pipeline can be created for further investigation. Azure Stream Analytics can be utilized on top of Event Hubs, and we can achieve storage along with serverless Azure Function services for internal processing, or use open-source tools such as Kafka if needed.
I personally work with Azure Stream Analytics for personal and smaller projects in a testing environment, in a lab environment, inside of this larger project. Azure Stream Analytics helps with processing fast-moving data in my IoT applications because it's quicker to escalate. We have a very good level of optimization process regarding costs. It's more elastic in our view. This is not totally confirmed, but in the smaller tests we made, this is very possible, and we have experience in Azure. We think it could be very optimized in the future, and we could optimize the performance process because the execution is faster inside of the same resource group, subscription, and overview. It's more integrated. In terms of optimizations, I think it could be much more reliable in terms of cost-benefit. We have a target for reduction in terms of performance of more than 40%, 50%, eventually more. I'm being very pessimistic. In terms of costs that we currently have, to not have this stream data implemented yet and fully automatically deployable, it's also a concern. If we don't have it, we have more costs in people to do it manually and to have these demands objectively implemented in a heavier way. If we have done this in different streams platforms, in different clouds, it will be harder to optimize in the time-lapses that we have. Azure Stream Analytics' event processing impacts my real-time financial transactions, but this is not a straight answer. There are some variables. For example, if you need to calculate some pricing, and you need some variables in that trigger event, you have to have a more delayed process on your side. The development and execution of those transactions could take a little bit longer. I was talking about re-engineering the process because we could receive this from the master data systems, already prepared. We are doing everything on our own, and I want to discharge this to the specific applications that do already that calculation with some algorithms that they have but we do not apply it. Then we can receive it. However, these transactions that occur right now, in terms of high-level understanding and evaluation on my part, are quite responsive to our needs.
I was monitoring and analyzing data from Azure Stream Analytics infrastructure. I do not remember examples of the main use cases for analytics or my most common use cases for this tool.
Data Strategist, Cloud Solutions Architect at BiTQ
Real User
Top 5
May 30, 2025
My main use cases for Azure Stream Analytics involve processing telemetry data coming from various devices, specifically sensors from mining. Predictive maintenance has been one of my use cases to design for with Azure Stream Analytics. Azure Stream Analytics helps me predict and mitigate potential failures, and one specific example I could provide is monitoring incoming temperatures. I am building messaging applications that look at temperature data from outside, processing that, and providing reporting to my customers to indicate the impact of weather changes. Based on that, they make business decisions on how they will reroute their product deliveries and other related matters.
Director, Governance & Infrastructure & Director at VASS
Real User
Top 5
Jan 28, 2025
Currently, I am working extensively with Microsoft Azure, and I also have some experience with Google, AWS, Snowflake, and Databricks. I work mainly with the primary vendors in the data landscape at present. I am looking for new partners, and Palantir seems to be a good option. Microsoft Azure is used for many tasks, and I also undertake some projects around OpenAI in Azure and ChargeDpT.
Program Manager at a manufacturing company with 10,001+ employees
Real User
Top 10
Nov 12, 2024
The critical use case was managing telemetry data, as well as multiple messaging topics coming from various sensors or triggering points, such as logistics events. These were handled to capture real-time use cases from IoT devices.
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.
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.
Our primary use case involves using the app centre to retrieve lifeline data. However, the lifeline is not retrievable from the app centre due to recent changes, so we get it from Azure.
Associate Principal Analyst at a computer software company with 10,001+ employees
Real User
Sep 24, 2021
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.
The company I'm working for is basically one of the biggest companies in the entire Gulf region, including Dubai, Qatar, and Oman. Our core domain is providing logistics. They have different warehouses across the country, and we use it to track the movement of forklifts and people working at the warehouses. The main thing we are focusing on right now is accident avoidance. For example, one forklift is coming through one aisle, and another person is trying to enter the same aisle. We provide a solution that can track the person and forklift in real-time. We're also using it to solve many business problems one by one. Stream Analytics plays a major role in streaming all these huge datasets because we have warehouses spread across the country. It's able to handle millions and millions of events in a few seconds.
Collaboration Consultant at a tech services company with 201-500 employees
Consultant
Oct 11, 2020
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.
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.
Assistant Director - IT Governance Support at a insurance company with 1,001-5,000 employees
Real User
Aug 22, 2018
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.
Azure Stream Analytics offers real-time data processing with seamless IoT hub integration and user-friendly setup. It efficiently manages data streams and supports Azure services, SQL Server, and Cosmos DB.Azure Stream Analytics specializes in real-time data analytics, easily integrating with Microsoft technologies. It enables swift deployment, monitoring, and high-performance data streaming. Though praised for its powerful SQL language and machine learning capabilities, users face challenges...
I have worked on many use cases involving IoT platforms over the last 10 years. For instance, with a large client in the U.S., I helped track vehicles for Paccar and other companies such as Amazon, URS, or UPS. They monitor many vehicles traveling across the U.S., capturing data regarding vehicles' problems or accidents. This information is processed using Kafka or Event Hubs and pushed to our platform for analytics, where report tools such as Power BI visualize data. For example, if any telemetry or real analytics data or streaming data comes from IoT, Azure provides the combination of Event Hubs and Streaming Analytics, and in open source, Kafka is used. For inter-system communication, the pub and sub model allows some services to publish data to the queue or topic in any services such as RabbitMQ, while Azure offers Service Bus. If you want to use real-time telemetry data, capturing things such as share market data or vehicles on highways, Event Hubs is recommended to pull that data. We can validate basic data schema using Azure Stream Analytics, determining if data processing should succeed or fail; if it succeeds, we process it with our platform, specifically our ADLS Gen2. For any basic validation that fails, another pipeline can be created for further investigation. Azure Stream Analytics can be utilized on top of Event Hubs, and we can achieve storage along with serverless Azure Function services for internal processing, or use open-source tools such as Kafka if needed.
I personally work with Azure Stream Analytics for personal and smaller projects in a testing environment, in a lab environment, inside of this larger project. Azure Stream Analytics helps with processing fast-moving data in my IoT applications because it's quicker to escalate. We have a very good level of optimization process regarding costs. It's more elastic in our view. This is not totally confirmed, but in the smaller tests we made, this is very possible, and we have experience in Azure. We think it could be very optimized in the future, and we could optimize the performance process because the execution is faster inside of the same resource group, subscription, and overview. It's more integrated. In terms of optimizations, I think it could be much more reliable in terms of cost-benefit. We have a target for reduction in terms of performance of more than 40%, 50%, eventually more. I'm being very pessimistic. In terms of costs that we currently have, to not have this stream data implemented yet and fully automatically deployable, it's also a concern. If we don't have it, we have more costs in people to do it manually and to have these demands objectively implemented in a heavier way. If we have done this in different streams platforms, in different clouds, it will be harder to optimize in the time-lapses that we have. Azure Stream Analytics' event processing impacts my real-time financial transactions, but this is not a straight answer. There are some variables. For example, if you need to calculate some pricing, and you need some variables in that trigger event, you have to have a more delayed process on your side. The development and execution of those transactions could take a little bit longer. I was talking about re-engineering the process because we could receive this from the master data systems, already prepared. We are doing everything on our own, and I want to discharge this to the specific applications that do already that calculation with some algorithms that they have but we do not apply it. Then we can receive it. However, these transactions that occur right now, in terms of high-level understanding and evaluation on my part, are quite responsive to our needs.
I was monitoring and analyzing data from Azure Stream Analytics infrastructure. I do not remember examples of the main use cases for analytics or my most common use cases for this tool.
My main use cases for Azure Stream Analytics involve processing telemetry data coming from various devices, specifically sensors from mining. Predictive maintenance has been one of my use cases to design for with Azure Stream Analytics. Azure Stream Analytics helps me predict and mitigate potential failures, and one specific example I could provide is monitoring incoming temperatures. I am building messaging applications that look at temperature data from outside, processing that, and providing reporting to my customers to indicate the impact of weather changes. Based on that, they make business decisions on how they will reroute their product deliveries and other related matters.
We use Azure Stream Analytics ( /products/azure-stream-analytics-reviews ) for data governance or ERP ( /categories/erp ) data analytics.
Currently, I am working extensively with Microsoft Azure, and I also have some experience with Google, AWS, Snowflake, and Databricks. I work mainly with the primary vendors in the data landscape at present. I am looking for new partners, and Palantir seems to be a good option. Microsoft Azure is used for many tasks, and I also undertake some projects around OpenAI in Azure and ChargeDpT.
The critical use case was managing telemetry data, as well as multiple messaging topics coming from various sensors or triggering points, such as logistics events. These were handled to capture real-time use cases from IoT devices.
Azure Stream Analytics is a simple tool used to deploy and implement.
I use the solution in my company for real-time analytics on IoT data.
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.
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.
We use the solution for real-time data and machine learning features.
The product is used just for the extraction, transforming, and loading of the data to the data warehouse.
Our primary use case involves using the app centre to retrieve lifeline data. However, the lifeline is not retrievable from the app centre due to recent changes, so we get it from Azure.
It's used primarily for data and mining - everything from the telemetry data side of things.
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.
The company I'm working for is basically one of the biggest companies in the entire Gulf region, including Dubai, Qatar, and Oman. Our core domain is providing logistics. They have different warehouses across the country, and we use it to track the movement of forklifts and people working at the warehouses. The main thing we are focusing on right now is accident avoidance. For example, one forklift is coming through one aisle, and another person is trying to enter the same aisle. We provide a solution that can track the person and forklift in real-time. We're also using it to solve many business problems one by one. Stream Analytics plays a major role in streaming all these huge datasets because we have warehouses spread across the country. It's able to handle millions and millions of events in a few seconds.
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.
Our primary use case is mainly to ingest real time data streams into permanent storage places like databases, block storage, etc.
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 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.