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 solution has a lot of functionality that can be pushed out to companies."
"Real-time analytics is the most valuable feature of this solution. I can send the collected data to Power BI in real time."
"The solution's most valuable feature is its ability to create a query using SQ."
"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's scalable as a cloud product."
"The way it organizes data into tables and dashboards is very helpful."
"The most valuable features are the IoT hub and the Blob storage."
"Technical support is pretty helpful."
"Imageflow is a visual tool that helps make it easier for business people to understand complex workflows."
"The solution is very simple and stable."
"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."
"The initial setup phase of Databricks was good."
"A very valuable feature is the data processing, and the solution is specifically good at using the Spark ecosystem."
"Databricks' most valuable features are the workspace and notebooks. Its integration, interface, and documentation are also good."
"The ease of use and its accessibility are valuable."
"The Delta Lake data type has been the most useful part of this solution. Delta Lake is an opensource data type and it was implemented and invented by Databricks."
"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."
"Early in the process, we had some issues with stability."
"The solution's interface could be simpler to understand for non-technical people."
"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."
"The solution could be improved by providing better graphics and including support for UI and UX testing."
"The initial setup is complex."
"The collection and analysis of historical data could be better."
"I would like to see the integration between Databricks and MLflow improved. It is quite hard to train multiple models in parallel in the distributed fashions. You hit rate limits on the clients very fast."
"The product should incorporate more learning aspects. It needs to have a free trial version that the team can practice."
"There is room for improvement in the documentation of processes and how it works."
"The product should provide more advanced features in future releases."
"When I used the support, I had communication problems because of the language barrier with the agent. The accent was difficult to understand."
"Implementation of Databricks is still very code heavy."
"The stability of the clusters or the instances of Databricks would be better if it was a much more stable environment. We've had issues with crashes."
"Databricks' performance when serving the data to an analytics tool isn't as good as Snowflake's."
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.