We compared RapidMiner and KNIME based on our user's reviews in several parameters.
RapidMiner stands out for its advanced machine learning algorithms, extensive pre-built models, and active community support, while KNIME is praised for its easy-to-use interface, extensive library of nodes, and excellent customer service. Users note that RapidMiner offers more flexibility and scalability, while KNIME is considered more user-friendly. Both have affordable pricing and positive ROI, but users suggest improvements in documentation and performance for RapidMiner, and enhancements in interface, tutorials, and advanced features for KNIME.
Features: RapidMiner stands out for its user-friendly interface, intuitive data visualization, powerful data preparation and analysis capabilities, and advanced machine learning algorithms. On the other hand, KNIME is praised for its ease of use, powerful data manipulation, extensive library of nodes, and ability to handle big data. Both offer excellent visualizations and seamless integration with other tools and platforms.
Pricing and ROI: In terms of setup cost, RapidMiner offers affordable and flexible pricing options, with a straightforward and transparent licensing approach. On the other hand, KNIME has minimal setup cost and a flexible licensing approach that accommodates the needs of different users and organizations., Based on user feedback, RapidMiner demonstrated positive ROI with increased efficiency, cost savings, and improved decision-making. KNIME also showed favorable ROI with users satisfied with the platform's value.
Room for Improvement: Users have mentioned that RapidMiner could benefit from better documentation and tutorials to help beginners navigate the platform more easily. Additionally, the user interface could be more intuitive and user-friendly. Some users have also suggested improved performance for larger datasets. On the other hand, KNIME users have expressed a desire for a more intuitive interface, better documentation, and tutorials. They have also mentioned performance and speed optimizations, as well as integrating more advanced analytics and machine learning capabilities.
Deployment and customer support: The user reviews suggest that the duration required for establishing a new tech solution can vary between RapidMiner and KNIME. Some RapidMiner users reported spending three months on deployment and an additional week on setup, while others mentioned needing a week for both deployment and setup. KNIME users also had similar experiences, with some spending three months on deployment and a week on setup, while others only needed a week for both tasks. It is important to consider the context in which these terms are used to accurately analyze the timeframes., RapidMiner and KNIME both offer excellent customer service. Users appreciate RapidMiner's helpfulness and responsiveness, while KNIME's support team is praised for their prompt and reliable assistance.
The summary above is based on 27 interviews we conducted recently with RapidMiner and KNIME users. To access the review's full transcripts, download our report.
"What I like most about KNIME is that it's user-friendly. It's a low-code, no-code tool, so students don't need coding knowledge. You can make use of different kinds of nodes. KNIME even has a good description of each node."
"The most valuable feature is the data wrangling, which is what I mainly use it for."
"It also has very good fundamental machine learning. It has decision trees, linear regression, and neural nets. It has a lot of text mining facilities as well. It's fairly fully-featured."
"It provides very fast problem solving and I don't need to do much coding in it. I just drag and drop."
"It can handle an unlimited amount of data, which is the advantage of using Knime."
"The visual workflow tools for custom and complex tasks always beat raw coding languages with the agility, speed to deliver, and ease of subsequent changes."
"The solution is good for teaching, since there is no need to code."
"This solution is easy to use and especially good at data preparation and wrapping."
"The solution is stable."
"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."
"Scalability is not really a concern with RapidMiner. It scales very well and can be used in global implementations."
"The documentation for this solution is very good, where each operator is explained with how to use it."
"RapidMiner is very easy to use."
"The most valuable features are the Binary classification and Auto Model."
"RapidMiner for Windows is an excellent graphical tool for data science."
"The most valuable feature is what the product sets out to do, which is extracting information and data."
"I would like to see better web scraping because every time I tried, it was not up to par, although you can use Python script."
"One thing to consider is that the prebuilt nodes may not always be a perfect fit for your specific needs, although most of the time, they work quite well."
"It could input more data acquisitions from other sources and it is difficult to combine with Python."
"I would prefer to have more connectivity."
"The visualization functionalities are not good (cannot be compared to, for instance, the possibilities in R)."
"The pricing needs improvement."
"They should look at other vendors like Alteryx that are more user friendly and modern."
"They could add more detailed examples of the functionality of every node, how it works and how we can use it, to make things easier at the beginning."
"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."
"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 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."
"I think that they should make deep learning models easier."
"If they could include video tutorials, people would find that quite helpful."
KNIME is ranked 4th in Data Science Platforms with 50 reviews while RapidMiner is ranked 7th in Data Science Platforms with 19 reviews. KNIME is rated 8.2, while RapidMiner is rated 8.6. The top reviewer of KNIME writes "A low-code platform that reduces data mining time by linking script". On the other hand, the top reviewer of RapidMiner writes "Offers good tutorials that make it easy to learn and use, with a powerful feature to compare machine learning algorithms". KNIME is most compared with Microsoft Power BI, Alteryx, Weka, Dataiku Data Science Studio and IBM SPSS Modeler, whereas RapidMiner is most compared with Alteryx, Dataiku Data Science Studio, Tableau, Microsoft Azure Machine Learning Studio and IBM SPSS Modeler. See our KNIME vs. RapidMiner report.
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Of those three you should consider alteryx, it saves time in ETL a lot, Alteryx is better at handling large data sets tan Knime and RapidMiner. But please also consider Dataiku... Up to 3 users it's free ;o)