We performed a comparison between Databricks and RapidMiner based on real PeerSpot user reviews.
Find out in this report how the two Data Science Platforms solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."The solution's features are fantastic and include interactive clusters that perform at top speed when compared to other solutions."
"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."
"It's very simple to use Databricks Apache Spark."
"Databricks is hosted on the cloud. It is very easy to collaborate with other team members who are working on it. It is production-ready code, and scheduling the jobs is easy."
"The solution is very easy to use."
"I work in the data science field and I found Databricks to be very useful."
"The main features of the solution are efficiency."
"The integration with Python and the notebooks really helps."
"Using the GUI, I can have models and algorithms drag and drop nodes."
"Scalability is not really a concern with RapidMiner. It scales very well and can be used in global implementations."
"The most valuable features are the Binary classification and Auto Model."
"The data science, collaboration, and IDN are very, very strong."
"I like not having to write all solutions from code. Being able to drag and drop controls, enables me to focus on building the best model, without needing to search for syntax errors or extra libraries."
"The most valuable feature of RapidMiner is that it can read a large number of file formats including CSV, Excel, and in particular, SPSS."
"The GUI capabilities of the solution are excellent. Their Auto ML model provides for even non-coder data scientists to deploy a model."
"RapidMiner is a no-code machine learning tool. I can install it on my local machine and work with smaller datasets. It can also connect to databases, allowing me to build models directly on the data stored there. RapidMiner offers a wider range of operators than other tools like Dataiku, making it a better option for my needs."
"I would love an integration in my desktop IDE. For now, I have to code on their webpage."
"Databricks would benefit from enhanced metrics and tighter integration with Azure's diagnostics."
"The interface of Databricks could be easier to use when compared to other solutions. It is not easy for non-data scientists. The user interface is important before we had to write code manually and as solutions move to "No code AI" it is critical that the interface is very good."
"This solution only supports queries in SQL and Python, which is a bit limiting."
"There is room for improvement in visualization."
"Overall it's a good product, however, it doesn't do well against any individual best-of-breed products."
"Costs can quickly add up if you don't plan for it."
"Some of the error messages that we receive are too vague, saying things like "unknown exception", and these should be improved to make it easier for developers to debug problems."
"I would appreciate improvements in automation and customization options to further streamline processes."
"The server product has been getting updated and continues to be better each release. When I started using RapidMiner, it was solid but not easy to set up and upgrade."
"RapidMiner can improve deep learning by enhancing the features."
"The biggest problem, not from a platform process, but from an avoidance process, is when you work in a heavily regulated environment, like banking and finance. Whenever you make a decision or there is an output, you need to bill it as an avoidance to the investigator or to the bank audit team. If you made decisions within this machine learning model, you need to explain why you did so. It would better if you could explain your decision in terms of delivery. However, this is an issue with all ML platforms. Many companies are working heavily in this area to help figure out how to make it more explainable to the business team or the regulator."
"The visual interface could use something like the-drag-and-drop features which other products already support. Some additional features can make RapidMiner a better tool and maybe more competitive."
"The price of this solution should be improved."
"RapidMiner would be improved with the inclusion of more machine learning algorithms for generating time-series forecasting models."
"In terms of the UI and SaaS, the user interface with KNIME is more appealing than RapidMiner."
Databricks is ranked 1st in Data Science Platforms with 78 reviews while RapidMiner is ranked 6th in Data Science Platforms with 20 reviews. Databricks is rated 8.2, while RapidMiner is rated 8.6. 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 RapidMiner writes "A no-code tool that helps to build machine learning models ". Databricks is most compared with Amazon SageMaker, Informatica PowerCenter, Dataiku, Dremio and Microsoft Azure Machine Learning Studio, whereas RapidMiner is most compared with KNIME, Alteryx, Dataiku, Tableau and Microsoft Power BI. See our Databricks vs. RapidMiner report.
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