We performed a comparison between Databricks and Starburst Enterprise based on real PeerSpot user reviews.
Find out what your peers are saying about Databricks, Microsoft, Alteryx and others in Data Science Platforms."Easy to use and requires minimal coding and customizations."
"Can cut across the entire ecosystem of open source technology to give an extra level of getting the transformatory process of the data."
"We like that this solution can handle a wide variety and velocity of data engineering, either in batch mode or real-time."
"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."
"It's easy to increase performance as required."
"Databricks' most valuable feature is the data transformation through PySpark."
"It's very simple to use Databricks Apache Spark."
"I work in the data science field and I found Databricks to be very useful."
"We have noticed improvements in performance using Starburst Enterprise. It handles complex data, including reading and partitioning files. We can add a new catalog to Starburst Enterprise by providing connection details and service account information. This allows us to integrate with existing tools, such as the Snowflake database, which we use for data protection in our project."
"The solution could be improved by integrating it with data packets. Right now, the load tables provide a function, like team collaboration. Still, it's unclear as to if there's a function to create different branches and/or more branches. Our team had used data packets before, however, I feel it's difficult to integrate the current with the previous data packets."
"Databricks is not geared towards the end-user, but rather it is for data engineers or data scientists."
"We'd like a more visual dashboard for analysis It needs better UI."
"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."
"Implementation of Databricks is still very code heavy."
"It's not easy to use, and they need a better UI."
"I believe that this product could be improved by becoming more user-friendly."
"The integration and query capabilities can be improved."
"Starburst Enterprise could improve by offering additional features similar to those provided by other SQL query tools. For example, incorporating functionalities like pivot tables would make it more feasible to use."
Earn 20 points
Databricks is ranked 1st in Data Science Platforms with 78 reviews while Starburst Enterprise is ranked 14th in Data Science Platforms with 1 review. Databricks is rated 8.2, while Starburst Enterprise is rated 8.0. The top reviewer of Databricks writes "A nice interface with good features for turning off clusters to save on computing". On the other hand, the top reviewer of Starburst Enterprise writes "Handles complex data and improves performance ". Databricks is most compared with Amazon SageMaker, Informatica PowerCenter, Dataiku, Dremio and Microsoft Azure Machine Learning Studio, whereas Starburst Enterprise is most compared with Dremio, Starburst Galaxy, Alteryx, Apache Spark Streaming and Informatica Data Engineering Streaming.
See our list of best Data Science Platforms vendors and best Streaming Analytics vendors.
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