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."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."
"If many teams are collaborating and sharing Jupyter notebooks, it's very useful."
"Data Science Studio's data science model is very useful."
"I like the interface, which is probably my favorite part of the solution. It is really user-friendly for an IT person."
"The solution is quite stable."
"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."
"Cloud-based process run helps in not keeping the systems on while processes are running."
"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 documentation for this solution is very good, where each operator is explained with how to use it."
"What I like about RapidMiner is its all-in-one nature, which allows me to prepare, extract, transform, and load data within the same tool."
"The best part of RapidMiner is efficiency."
"RapidMiner for Windows is an excellent graphical tool for data science."
"I've been using a lot of components from the Strategic Extension and Python Extension."
"It is easy to use and has a huge community that I can rely on for help. Moreover, it is interactive."
"The GUI capabilities of the solution are excellent. Their Auto ML model provides for even non-coder data scientists to deploy a model."
"I think it would help if Data Science Studio added some more features and improved the data model."
"Although known for Big Data, the processing time to process 1.8 billion records was terribly slow (five days)."
"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."
"I find that it is a little slow during use. It takes more time than I would expect for operations to complete."
"Server up-time needs to be improved. Also, query engines like Spark and Hive need to be more stable."
"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."
"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 ability to have charts right from the explorer would be an improvement."
"I would like to see more integration capabilities."
"I would like to see all users have access to all of the deep learning models, and that they can be used easily."
"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."
"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."
"It would be helpful to have some tutorials on communicating with Python."
"RapidMiner can improve deep learning by enhancing the features."
"In the Mexican or Latin American market, it's kind of pricey."
"I would appreciate improvements in automation and customization options to further streamline processes."
Dataiku is ranked 11th 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 "The model is very useful". 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.
See our list of best Data Science Platforms vendors.
We monitor all Data Science Platforms 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.
