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 are the IoT hub and the Blob storage."
"Real-time analytics is the most valuable feature of this solution. I can send the collected data to Power BI in real time."
"I like the IoT part. We have mostly used Azure Stream Analytics services for it"
"The most valuable features of Azure Stream Analytics are the ease of provisioning and the interface is not terribly complex."
"We use Azure Stream Analytics for simulation and internal activities."
"The way it organizes data into tables and dashboards is very helpful."
"The solution has a lot of functionality that can be pushed out to companies."
"I like all the connected ecosystems of Microsoft, it is really good with other BI tools that are easy to connect."
"The solution is built from Spark and has integration with MLflow, which is important for our use case."
"It is a cost-effective solution."
"The setup is quite easy."
"I work in the data science field and I found Databricks to be very useful."
"The time travel feature is the solution's most valuable aspect."
"The solution is very simple and stable."
"Databricks gives you the flexibility of using several programming languages independently or in combination to build models."
"Databricks has a scalable Spark cluster creation process. The creators of Databricks are also the creators of Spark, and they are the industry leaders in terms of performance."
"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 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."
"Its features for event imports and architecture could be enhanced."
"The solution offers a free trial, however, it is too short."
"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 initial setup is complex."
"Early in the process, we had some issues with stability."
"This solution only supports queries in SQL and Python, which is a bit limiting."
"When I used the support, I had communication problems because of the language barrier with the agent. The accent was difficult to understand."
"Databricks would have more collaborative features than it has. It should have some more customization for the jobs."
"There should be better integration with other platforms."
"There is room for improvement in the documentation of processes and how it works."
"I would love an integration in my desktop IDE. For now, I have to code on their webpage."
"The query plan is not easy with Databrick's job level. If I want to tune any of the code, it is not easily available in the blogs as well."
"There is room for improvement in visualization."
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.