Azure Stream Analytics Reviews

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.

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.

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 use Azure Stream Analytics for simulation and internal activities.

The solution’s customer support could be improved.

I have been using Azure Stream Analytics for more than two years.

Azure Stream Analytics is a stable solution.

I rate Azure Stream Analytics a ten out of ten for scalability.

I rate Azure Stream Analytics a nine out of ten for its ease of initial setup.

Azure Stream Analytics is a little bit expensive.

Overall, I rate Azure Stream Analytics ten out of ten.

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.

The solution's most valuable feature is the machine learning functionality. It provides the capability to streamline multiple output components.

The solution's query languages must be more comprehensive. Also, its features for event imports and architecture need enhancement.

I have been using the solution for five years.

It is a stable solution. Although, sometimes, we encounter downtime issues.

The response time of the solution's technical support team depends on the criticality of the issue and the SLA subscription plan.

Positive

Databricks works with Python providing more capabilities and flexibilities than Azure Stream Analytics.

The solution's initial setup is straightforward when configuring inputs and outputs.

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.

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.

Positive

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 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.