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."
"We find the query editor feature of this solution extremely valuable for our business."
"It's scalable as a cloud product."
"The most valuable features are the IoT hub and the Blob storage."
"The way it organizes data into tables and dashboards is very helpful."
"Real-time analytics is the most valuable feature of this solution. I can send the collected data to Power BI in real time."
"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."
"It is fast, it's scalable, and it does the job it needs to do."
"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."
"Databricks provides a consistent interface for data engineers to work with data in a consistent language on a single integrated platform for ingesting, processing, and serving data to the end user."
"The most valuable feature of Databricks is the notebook, data factory, and ease of use."
"The most valuable aspect of the solution is its notebook. It's quite convenient to use, both terms of the research and the development and also the final deployment, I can just declare the spark jobs by the load tables. It's quite convenient."
"Imageflow is a visual tool that helps make it easier for business people to understand complex workflows."
"The most valuable feature is the ability to use SQL directly with Databricks."
"We have the ability to scale, collaborate and do machine learning."
"The solution’s customer support could be improved."
"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 offers a free trial, however, it is too short."
"The UI should be a little bit better from a usability perspective."
"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."
"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."
"Sometimes when we connect Power BI, there is a delay or it throws up some errors, so we're not sure."
"Doesn't provide a lot of credits or trial options."
"Databricks doesn't offer the use of Python scripts by itself and is not connected to GitHub repositories or anything similar. This is something that is missing. if they could integrate with Git tools it would be an advantage."
"It's not easy to use, and they need a better UI."
"There is room for improvement in visualization."
"It should have more compatible and more advanced visualization and machine learning libraries."
"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."
"It would be nice to have more guidance on integrations with ETLs and other data quality tools."
"Instead of relying on a massive instance, the solution should offer micro partition levels. They're working on it, however, they need to implement it to help the solution run more effectively."
Azure Stream Analytics is ranked 4th in Streaming Analytics with 21 reviews while Databricks is ranked 1st in Streaming Analytics with 77 reviews. Azure Stream Analytics is rated 8.0, 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, Microsoft Azure Machine Learning Studio, Dataiku Data Science Studio and Dremio. 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.