Azure Stream Analytics Reviews

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.

It was easy for me to use from the beginning. I am accustomed to working with Microsoft. It is quite easy for my technicians to understand, and the learning curve is not steep. This is the best aspect for me, along with the native connectors and integration with other Microsoft products. 

It is just a part of React integrators. Sometimes I perform maintenance, but not as a managed service provider. I deal with several systems, like ERPs, CRMs, and so on, to process the data and present it to our management.

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. 

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. 

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 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.

The most valuable aspect is the SQL option that Azure Stream Analytics provides. These inbuilt solutions offer more flexibility within an enterprise level. If we don't use Stream Analytics, which has features that facilitate direct data transportation to end destinations, we might have to manage complex setups or activities coordination, potentially increasing costs.

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.

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.