We performed a comparison between Azure Stream Analytics and Databricks based on our users’ reviews in five categories. After reading all of the collected data, you can find our conclusion below.
Comparison Results: Databricks is the winner in this comparison. It is stable and powerful with good machine learning features. Azure Stream Analytics does come out on top in the pricing category, however.
"Real-time analytics is the most valuable feature of this solution. I can send the collected data to Power BI in real time."
"The most valuable features of Azure Stream Analytics are the ease of provisioning and the interface is not terribly complex."
"The solution has a lot of functionality that can be pushed out to companies."
"Provides deep integration with other Azure resources."
"I like the way the UI looks, and the real-time analytics service is aligned to this. That can be helpful if I have to use this on a production service."
"The solution's technical support is good."
"It's a product that can scale."
"The integrations for this solution are easy to use and there is flexibility in integrating the tool with Azure Stream Analytics."
"The solution is built from Spark and has integration with MLflow, which is important for our use case."
"The ease of use and its accessibility are valuable."
"Can cut across the entire ecosystem of open source technology to give an extra level of getting the transformatory process of the data."
"The most valuable feature of Databricks is the notebook, data factory, and ease of use."
"This solution offers a lake house data concept that we have found exciting. We are able to have a large amount of data in a data lake and can manage all relational activities."
"Databricks has helped us have a good presence in data."
"Databricks gives us the ability to build a lakehouse framework and do everything implicit to this type of database structure. We also like the ability to stream events. Databricks covers a broad spectrum, from reporting and machine learning to streaming events. It's important for us to have all these features in one platform."
"The most valuable feature of Databricks is the integration with Microsoft Azure."
"If something goes wrong, it's very hard to investigate what caused it and why."
"The UI should be a little bit better from a usability perspective."
"The solution's interface could be simpler to understand for non-technical people."
"There may be some issues when connecting with Microsoft Power BI because we are providing the input and output commands, and there's a chance of it being delayed while connecting."
"Its features for event imports and architecture could be enhanced."
"Easier scalability and more detailed job monitoring features would be helpful."
"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 would like to have a contact individual at Microsoft."
"I would like to see more documentation in terms of how an end-user could use it, and users like me can easily try it and implement use cases."
"It would be better if it were faster. It can be slow, and it can be super fast for big data. But for small data, sometimes there is a sub-second response, which can be considered slow. In the next release, I would like to have automatic creation of APIs because they don't have it at the moment, and I spend a lot of time building them."
"Would be helpful to have additional licensing options."
"Generative AI is catching up in areas like data governance and enterprise flavor. Hence, these are places where Databricks has to be faster."
"The product needs samples and templates to help invite users to see results and understand what the product can do."
"Databricks is an analytics platform. It should offer more data science. It should have more features for data scientists to work with."
"Can be improved by including drag-and-drop features."
"The interface of Databricks could be easier to use when compared to other solutions. It is not easy for non-data scientists. The user interface is important before we had to write code manually and as solutions move to "No code AI" it is critical that the interface is very good."
Azure Stream Analytics is ranked 3rd in Streaming Analytics with 22 reviews while Databricks is ranked 2nd in Streaming Analytics with 78 reviews. Azure Stream Analytics is rated 8.2, while Databricks is rated 8.2. The top reviewer of Azure Stream Analytics writes "Easy to set up and user-friendly, but could be priced better". On the other hand, the top reviewer of Databricks writes "A nice interface with good features for turning off clusters to save on computing". Azure Stream Analytics is most compared with Amazon Kinesis, Amazon MSK, Apache Flink, Apache Spark and Apache Spark Streaming, whereas Databricks is most compared with Amazon SageMaker, Informatica PowerCenter, Dataiku, Microsoft Azure Machine Learning Studio and Google Cloud Dataflow. See our Azure Stream Analytics vs. Databricks report.
See our list of best Streaming Analytics vendors.
We monitor all Streaming Analytics reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.