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.
"Technical support is pretty helpful."
"I like all the connected ecosystems of Microsoft, it is really good with other BI tools that are easy to connect."
"The life cycle, report management and crash management features are great."
"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."
"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 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."
"It provides the capability to streamline multiple output components."
"The most valuable features of Azure Stream Analytics are the ease of provisioning and the interface is not terribly complex."
"A very valuable feature is the data processing, and the solution is specifically good at using the Spark ecosystem."
"Imageflow is a visual tool that helps make it easier for business people to understand complex workflows."
"The most valuable feature of Databricks is the integration with Microsoft Azure."
"The technical support is good."
"Databricks covers end-to-end data analytics workflow in one platform, this is the best feature of the solution."
"The initial setup phase of Databricks was good."
"The most valuable feature is the Spark cluster which is very fast for heavy loads, big data processing and Pi Spark."
"The time travel feature is the solution's most valuable aspect."
"Its features for event imports and architecture could be enhanced."
"The solution could be improved by providing better graphics and including support for UI and UX testing."
"Easier scalability and more detailed job monitoring features would be helpful."
"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."
"The UI should be a little bit better from a usability perspective."
"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."
"The solution doesn't handle large data packets very efficiently, which could be improved upon."
"The initial setup is complex."
"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'm not the guy that I'm working with Databricks on a daily basis. I'm on the management team. However, my team tells me there are limitations with streaming events. The connectors work with a small set of platforms. For example, we can work with Kafka, but if we want to move to an event-driven solution from AWS, we cannot do it. We cannot connect to all the streaming analytics platforms, so we are limited in choosing the best one."
"Databricks may not be as easy to use as other tools, but if you simplify a tool too much, it won't have the flexibility to go in-depth. Databricks is completely in the programmer's hands. I prefer flexibility rather than simplicity."
"Anyone who doesn't know SQL may find the product difficult to work with."
"Databricks is not geared towards the end-user, but rather it is for data engineers or data scientists."
"When I used the support, I had communication problems because of the language barrier with the agent. The accent was difficult to understand."
"The connectivity with various BI tools could be improved, specifically the performance and real time integration."
"The product cannot be integrated with a popular coding IDE."
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.