Azure Stream Analytics Reviews

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. 

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. 

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.

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.

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. 

I haven't come across missing items. It does what I need it to do.

The pricing is a little bit high.

The UI should be a little bit better from a usability perspective. The endpoint, if you are outsourcing to a third party, should have easier APIs. I'd like to have more destination sources available to us. 

I've been using the solution for one year. 

The solution has been stable. There are no bugs or glitches and it doesn't crash or freeze. It's reliable. 

We have around 50 people using the solution currently.

The solution can scale well. It's not a problem at all. 

Mainly the users are developers, DevOps, and the QA automation team.

They have excellent support. They're always helpful and always resolve issues for us.

Positive

We are also using Databricks.

Compared to Databricks, Azure Stream Analytics is clear and the stream management and the queue management of the stream, the enrichment of the data analytics capability of the stream, these features are very good.

The Databricks user interface and the programming and control are better than the Azure Stream Analytics. I need to make a lot of configurations here. The control and the Azure database is a totally different service in and of itself. It is built on Sparx and Huawei, and the programming languages, and writing the jobs are better than Stream Analytics. 

The initial setup is very simple and straightforward. I would rate it a five out of five. We didn't have any trouble with it. 

We handled the initial setup ourselves. We did not have any issues that would require any third-party assistance. 

We've seen around a 10% ROI.

We find the pricing to be a little bit higher side. If that could become a bit more competitive with, for example, AWS or something, that would be great.

We pay approximately $500,000 a year. It's approximately $10,000 a year per license. 

I'd rate it a three out of five in terms of affordability.

I'd 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.