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.
"The most valuable features of Azure Stream Analytics are the ease of provisioning and the interface is not terribly complex."
"Technical support is pretty helpful."
"The life cycle, report management and crash management features are great."
"I like the IoT part. We have mostly used Azure Stream Analytics services for it"
"We find the query editor feature of this solution extremely valuable for our business."
"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."
"The most valuable features are the IoT hub and the Blob storage."
"I like all the connected ecosystems of Microsoft, it is really good with other BI tools that are easy to connect."
"I like how easy it is to share your notebook with others. You can give people permission to read or edit. I think that's a great feature. You can also pull in code from GitHub pretty easily. I didn't use it that often, but I think that's a cool feature."
"In the manufacturing industry, Databricks can be beneficial to use because of machine learning. It is useful for tasks, such as product analysis or predictive maintenance."
"It's very simple to use Databricks Apache Spark."
"It is fast, it's scalable, and it does the job it needs to do."
"Databricks integrates well with other solutions."
"The initial setup phase of Databricks was good."
"We like that this solution can handle a wide variety and velocity of data engineering, either in batch mode or real-time."
"The load distribution capabilities are good, and you can perform data processing tasks very quickly."
"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."
"The solution could be improved by providing better graphics and including support for UI and UX testing."
"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."
"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."
"Easier scalability and more detailed job monitoring features would be helpful."
"I would like to have a contact individual at Microsoft."
"It is not complex, but it requires some development skills. When the data is sent from Azure Stream Analytics to Power BI, I don't have the access to modify the data. I can't customize or edit the data or do some queries. All queries need to be done in the Azure Stream Analytics."
"We would like to have centralized platform altogether since we have different kind of options for data ingestion. Sometimes it gets difficult to manage different platforms."
"The connectivity with various BI tools could be improved, specifically the performance and real time integration."
"I have seen better user interfaces, so that is something that can be improved."
"Databricks has added some alerts and query functionality into their SQL persona, but the whole SQL persona, which is like a role, needs a lot of development. The alerts are not very flexible, and the query interface itself is not as polished as the notebook interface that is used through the data science and machine learning persona. It is clunky at present."
"I would love an integration in my desktop IDE. For now, I have to code on their webpage."
"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."
"When I used the support, I had communication problems because of the language barrier with the agent. The accent was difficult to understand."
"Costs can quickly add up if you don't plan for it."
"Databricks could improve in some of its functionality."
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, Dremio 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.