We performed a comparison between IBM SPSS Modeler and KNIME based on real PeerSpot user reviews.
Find out in this report how the two Data Mining solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."We have integration where you can write third-party apps. This sort of feature opens it up to being able to do anything you want."
"We had an IBM Guardium service contract where we used one of their resources to help us develop our prototype. It was a good experience, but they were helpful and responsive."
"Some basic form of feature engineering for classification models. This really quickens the model development process."
"It gives you a GUI interface, which is a lot more user-friendly and easier to use compared to writing R scripts or Python."
"The supervised models are valuable. It is also very organized and easy to use."
"So far, the stability has been rock solid."
"I think the code modeling features are the most valuable and without the need to write a code back with many different possibilities to choose from. And the second one is linked to the activity of the data preparation."
"Our go live process has been slightly enhanced compared to the previous programmatic process. There is now a faster time to production from the business end."
"It can handle an unlimited amount of data, which is the advantage of using Knime."
"We are able to automate several functions which were done manually. I can integrate several data sets quickly and easily, to support analytics."
"We have found KNIME valuable when it comes to its visualization."
"This open-source product can compete with category leaders in ELT software."
"The most valuable feature is the data wrangling, which is what I mainly use it for."
"The most valuable features of KNIME are its ability to convert your sub-workflow into a node. For example, the workflow has many individual native nodes that can be converted into a single node. This representation has simplified my workflow to a great extent. I can present my workflow in a very compact way."
"KNIME is quite scalable, which is one of the most important features that we found."
"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."
"When I used it in the office, back in the day, we did have some stability issues. Sometimes it just randomly crashed and we couldn't get good feedback. But when I use it for my own stuff now I don't have any problems."
"The platform that you can deploy it on needs improvement because I think it is Windows only. I do not think it can run off a Red Hat, like the server products. I am pretty sure it is Windows and AIX only."
"Neural networks are quite simple, and now neural networks are evolving to these architecture related to deep learning, etc. They didn't incorporate this in IBM SPSS Modeler."
"It is very good, but slow. The slowness may be because we have not finalized all the background information in SPSS. It still needs some tweaking."
"C&DS will not meet our scalability needs."
"Customer support is hard to contact."
"The challenge for the very technical data scientists: It is constraining for them."
"I can say the solution is outdated."
"KNIME is not good at visualization."
"I would prefer to have more connectivity."
"I've had some problems integrating KNIME with other solutions."
"It's pretty straightforward to understand. So, if you understand what the pipeline is, you can use the drag-and-drop functionality without much training. Doing the same thing in Python requires so much more training. That's why I use KNIME."
"The overall user experience feels unpolished. In particular: Data field type conversion is a real hassle, and date fields are a hassle; documentation is pretty poor; user community is average at best."
"To enhance accessibility and user-friendliness, there is a need for improvements in the interface and usability of deep learning and large-scale learning languages."
"KNIME needs to provide more documentation and training materials, including webinars or online seminars."
"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."
IBM SPSS Modeler is ranked 4th in Data Mining with 38 reviews while KNIME is ranked 1st in Data Mining with 50 reviews. IBM SPSS Modeler is rated 8.0, while KNIME is rated 8.2. The top reviewer of IBM SPSS Modeler writes "Easy to use, quick to learn, and offers many ways to analyze data". On the other hand, the top reviewer of KNIME writes "A low-code platform that reduces data mining time by linking script". IBM SPSS Modeler is most compared with Microsoft Power BI, IBM SPSS Statistics, RapidMiner, Alteryx and SAS Visual Analytics, whereas KNIME is most compared with RapidMiner, Microsoft Power BI, Alteryx, Dataiku and Databricks. See our IBM SPSS Modeler vs. KNIME report.
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KNIME. It free, open-source, and you can plug in Java, Python, R, and Matlab. The community is awesome.
I used IBM Modeler several years ago and found it to be effective, but expensive. Fortunately, it was for a commercially funded contract.
For KNIME I have only used it for experimental purposes and found it rather cumbersome but powerful. It is also more cost-effective.
I found RapidMiner more intuitive to learn. However, there is so much choice nowadays that it is difficult to be definitive. In my experience, it largely depended on the quality of the add-on extensions. Clearly, though, at least in universities, the cost is a significant factor.
I am not familiar with KNIME, but the main difference is KNIME is open-source and free.