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."The solution is quite stable."
"I like the interface, which is probably my favorite part of the solution. It is really user-friendly for an IT person."
"Data Science Studio's data science model is very useful."
"Cloud-based process run helps in not keeping the systems on while processes are running."
"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."
"The most valuable feature is the set of visual data preparation tools."
"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."
"Scalability is not really a concern with RapidMiner. It scales very well and can be used in global implementations."
"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."
"The most valuable feature of RapidMiner is that it is code free. It is similar to playing with Lego pieces and executing after you are finished to see the results. Additionally, it is easy to use and has interesting utilities when preparing the data. It has a utility to automatically launch a series of models and show the comparisons. When finished with the comparisons you can select the best one, and deploy it automatically."
"The best part of RapidMiner is efficiency."
"The documentation for this solution is very good, where each operator is explained with how to use it."
"The solution is stable."
"RapidMiner is very easy to use."
"The data science, collaboration, and IDN are very, very strong."
"I find that it is a little slow during use. It takes more time than I would expect for operations to complete."
"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."
"Although known for Big Data, the processing time to process 1.8 billion records was terribly slow (five days)."
"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."
"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."
"The ability to have charts right from the explorer would be an improvement."
"I think it would help if Data Science Studio added some more features and improved the data model."
"I think that they should make deep learning models easier."
"RapidMiner isn't cheap. It's a complete solution, but it's costly."
"I would appreciate improvements in automation and customization options to further streamline processes."
"Many things in the interface look nice, but they aren't of much use to the operator. It already has lots of variables in there."
"The price of this solution should be improved."
"It would be helpful to have some tutorials on communicating with Python."
"In terms of the UI and SaaS, the user interface with KNIME is more appealing than RapidMiner."
"A great product but confusing in some way with regard to the user interface and integration with other tools."
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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