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 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."
"Data Science Studio's data science model is 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 the set of visual data preparation tools."
"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."
"Using the GUI, I can have models and algorithms drag and drop nodes."
"The documentation for this solution is very good, where each operator is explained with how to use it."
"The data science, collaboration, and IDN are very, very strong."
"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."
"RapidMiner for Windows is an excellent graphical tool for data science."
"The best part of RapidMiner is efficiency."
"The most valuable feature of RapidMiner is that it can read a large number of file formats including CSV, Excel, and in particular, SPSS."
"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."
"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 think it would help if Data Science Studio added some more features and improved the data model."
"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."
"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."
"Server up-time needs to be improved. Also, query engines like Spark and Hive need to be more stable."
"Dataiku still needs some coding, and that could be a difference where business data scientists would go for DataRobot more than Dataiku."
"Although known for Big Data, the processing time to process 1.8 billion records was terribly slow (five days)."
"In terms of the UI and SaaS, the user interface with KNIME is more appealing than RapidMiner."
"RapidMiner isn't cheap. It's a complete solution, but it's costly."
"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."
"I would appreciate improvements in automation and customization options to further streamline processes."
"I would like to see all users have access to all of the deep learning models, and that they can be used easily."
"If they could include video tutorials, people would find that quite helpful."
"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."
"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."
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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