We performed a comparison between Dataiku 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."I like the interface, which is probably my favorite part of the solution. It is really user-friendly for an IT person."
"The most valuable feature is the set of visual data preparation tools."
"The solution is quite stable."
"Data Science Studio's data science model is very useful."
"The most valuable feature of this solution is that it is one tool that can do everything, and you have the ability to very easily push your design to prediction."
"If many teams are collaborating and sharing Jupyter notebooks, it's very useful."
"Extremely easy to use with its GUI-based functionality and large compatibility with various data sources. Also, maintenance processes are much more automated than ever, with fewer errors."
"Cloud-based process run helps in not keeping the systems on while processes are running."
"The most valuable feature is what the product sets out to do, which is extracting information and data."
"The GUI capabilities of the solution are excellent. Their Auto ML model provides for even non-coder data scientists to deploy a model."
"Scalability is not really a concern with RapidMiner. It scales very well and can be used in global implementations."
"The data science, collaboration, and IDN are very, very strong."
"RapidMiner is very easy to use."
"Using the GUI, I can have models and algorithms drag and drop nodes."
"We value the collaboration and governance features because it's a comprehensive platform that covers everything from data extraction to modeling operations in the ML language. RapidMiner is competitive in the ML space."
"RapidMiner for Windows is an excellent graphical tool for data science."
"I think it would help if Data Science Studio added some more features and improved the data model."
"The interface for the web app can be a bit difficult. It needs to have better capabilities, at least for developers who like to code. This is due to the fact that everything is enabled in a single window with different tabs. For them to actually develop and do the concurrent testing that needs to be done, it takes a bit of time. That is one improvement that I would like to see - from a web app developer perspective."
"Although known for Big Data, the processing time to process 1.8 billion records was terribly slow (five days)."
"The ability to have charts right from the explorer would be an improvement."
"There were stability issues: 1) SQL operations, such as partitioning, had bugs and showed wrong results. 2) Due to server downtime, scheduled processes used to fail. 3) Access to project folders was compromised (privacy issue) with wrong people getting access to confidential project folders."
"Server up-time needs to be improved. Also, query engines like Spark and Hive need to be more stable."
"In the next release of this solution, I would like to see deep learning better integrated into the tool and not simply an extension or plugin."
"Dataiku still needs some coding, and that could be a difference where business data scientists would go for DataRobot more than Dataiku."
"I would like to see all users have access to all of the deep learning models, and that they can be used easily."
"I would appreciate improvements in automation and customization options to further streamline processes."
"A great product but confusing in some way with regard to the user interface and integration with other tools."
"One challenge I encountered while implementing RapidMiner was the lack of documentation. Since there aren't as many users, finding resources to learn the tool was initially difficult. To overcome this hurdle, I believe RapidMiner could improve by providing more tutorials tailored for new users."
"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."
"Improve the online data services."
"In terms of the UI and SaaS, the user interface with KNIME is more appealing than RapidMiner."
"I would like to see more integration capabilities."
Dataiku is ranked 7th in Data Science Platforms with 7 reviews while RapidMiner is ranked 6th in Data Science Platforms with 20 reviews. Dataiku is rated 8.2, while RapidMiner is rated 8.6. The top reviewer of Dataiku writes "Gives different aspects of modeling approaches and good for multiple teams' collaboration". On the other hand, the top reviewer of RapidMiner writes "A no-code tool that helps to build machine learning models ". Dataiku is most compared with Databricks, KNIME, Alteryx, Microsoft Azure Machine Learning Studio and Amazon SageMaker, whereas RapidMiner is most compared with KNIME, Alteryx, Tableau, Microsoft Azure Machine Learning Studio and IBM SPSS Modeler. See our Dataiku vs. RapidMiner report.
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