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.
"It provides very fast problem solving and I don't need to do much coding in it. I just drag and drop."
"It's a huge tool with machine learning features as well."
"Valuable features include visual workflow creation, workflow variables (parameterisation), automatic caching of all intermediate data sets in the workflow, scheduling with the server."
"Easy to connect with every database: We use queries from SQL, Redshift, Oracle."
"What I like the most is that it works almost out of the box with Random Forest and other Forest nodes."
"We have been able to appreciate the considerable reduction in prototyping time."
"The product is very easy to understand even for non-analytical stakeholders. Sometimes we provide them with KNIME workflows and teach them how to run it on their own machine."
"From a user-friendliness perspective, it's a great tool."
"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 of RapidMiner is that it can read a large number of file formats including CSV, Excel, and in particular, SPSS."
"The solution is stable."
"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."
"I've been using a lot of components from the Strategic Extension and Python Extension."
"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."
"Both RapidMiner and KNIME should be made easier to use in the field of deep learning."
"In my environment, I need to access a lot of servers with different characteristics and access methods. Some of my servers have to be accessed using proxy which is not supported by KNIME, so I still need to create the middleware to supply the source of my KNIME configurations."
"The ability to handle large amounts of data and performance in processing need to be improved."
"The dynamic column name feature could be improved. When attempting to automate processes involving columns, such as with companies, it becomes difficult to achieve the same result when we make changes."
"KNIME needs to provide more documentation and training materials, including webinars or online seminars."
"The pricing needs improvement."
"There should be better documentation and the steps should be easier."
"KNIME's licensing and data management aren't as straightforward relative to Alteryx. Alteryx's tools are more sophisticated, so you need fewer to use it compared to KNIME. I think tab implementation could be easier, too."
"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."
"RapidMiner would be improved with the inclusion of more machine learning algorithms for generating time-series forecasting models."
"It would be helpful to have some tutorials on communicating with Python."
"If they could include video tutorials, people would find that quite helpful."
"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."
"I think that they should make deep learning models easier."
"RapidMiner can improve deep learning by enhancing the features."
"In the Mexican or Latin American market, it's kind of pricey."
KNIME is ranked 4th in Data Science Platforms with 50 reviews while RapidMiner is ranked 6th in Data Science Platforms with 20 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 "A no-code tool that helps to build machine learning models ". KNIME is most compared with Microsoft Power BI, Alteryx, Dataiku, Weka and Microsoft Azure Machine Learning Studio, whereas RapidMiner is most compared with Alteryx, Dataiku, 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)