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.
"This solution is easy to use and it can be used to create any kind of model."
"We have found KNIME valuable when it comes to its visualization."
"There are a lot of connectors available in KNIME."
"Stability is excellent. I would give it a nine out of ten."
"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."
"We are able to automate several functions which were done manually. I can integrate several data sets quickly and easily, to support analytics."
"The most useful features are the readily available extensions that speed up the work."
"I was able to apply basic algorithms through just dragging and dropping."
"RapidMiner is very easy to use."
"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."
"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."
"The GUI capabilities of the solution are excellent. Their Auto ML model provides for even non-coder data scientists to deploy a model."
"The best part of RapidMiner is efficiency."
"The solution is stable."
"It is easy to use and has a huge community that I can rely on for help. Moreover, it is interactive."
"The most valuable feature is what the product sets out to do, which is extracting information and data."
"There are some parameters that I would like to have at a bigger scale. The upper limit of one node that tries to find spots or areas in photos was too small for us. It would need to be bigger."
"In the last update, KNIME started hiding a lot of the nodes. It doesn't mean hiding, but you need to know what you're looking for. Before that, you had just a tree that you could click, and you could get an overview of what kind of nodes do I have. Right now, it's like you need to know which node you need, and then you can start typing, but it's actually more difficult to find them."
"There are a lot of tools in the product and it would help if they were grouped into classes where you can select a function, rather than a specific tool."
"KNIME is not scalable."
"If they had a more structured training model it would be very helpful."
"The predefined workflows could use a bit of improvement."
"It could input more data acquisitions from other sources and it is difficult to combine with Python."
"KNIME could improve when it comes to large data markets."
"RapidMiner can improve deep learning by enhancing the features."
"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."
"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."
"Improve the online data services."
"I would like to see more integration capabilities."
"A great product but confusing in some way with regard to the user interface and integration with other tools."
"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."
"The price of this solution should be improved."
KNIME is ranked 4th in Data Science Platforms with 50 reviews while RapidMiner is ranked 6th 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)