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."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."
"The most valuable features of the IBM SPSS Modeler are visual programming, you don't have to write any code, and it is easy to use. 90 to 95 percent of the use cases, you don't have to fine-tune anything. If you want to do something deeper, for example, create a better neural network, then you have to go into the features and try to fine-tune them. However, the default selection which is made by the tool, it's very practical and works well."
"It scales. I have not run into any challenges where it will not perform."
"Very good data aggregation."
"I think it is the point and drag features that are the most valuable. You can simply click at the windows, and then pull up the functions."
"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."
"Stability is good."
"Extremely easy to use, it offers a generous selection of proprietary machine learning algorithms."
"The solution allows for sharing model designs and model operations with other data analysts."
"The ETL which helps me to collect, reformat, and load the data from multiple sources into one destination, a storage database."
"It's very convenient to write your own algorithms in KNIME. You can write it in Java script or Python transcript."
"It can handle an unlimited amount of data, which is the advantage of using Knime."
"It is very fast to develop solutions."
"Since KNIME is a no-code platform, it is easy to work with."
"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."
"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."
"C&DS will not meet our scalability needs."
"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."
"Customer support is hard to contact."
"Requires more development."
"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."
"The challenge for the very technical data scientists: It is constraining for them."
"Formula writing is not straightforward for an Excel user. Totally new set of functions, which takes time to learn and teach."
"We have run into a few problems doing some entity matching/analytics."
"It could be easier to use."
"It's difficult to provide input on the improvement area because it's more of self-learning. However, there are times when I am not able to do certain things. I don't know if it's because the solution doesn't allow me or if it's because of the lack of knowledge."
"Though I can use KNIME in a 64-bit platform in the lab, it's missing some features. For example, from my laptop, I can use the image reader feature of KNIME. However, in the lab, the image reader node is missing."
"KNIME could improve when it comes to large data markets."
"I would prefer to have more connectivity."
"The license is quite expensive for us."
"The solution is inconvenient when it comes to wrangling data that includes multiple steps or features because each step or feature requires its own icon."
"The most difficult part of the solution revolves around its areas concerning machine learning and deep learning."
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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We monitor all Data Mining reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.
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.