We performed a comparison between KNIME and SAS Enterprise Miner 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."There are a lot of connectors available in KNIME."
"It allows for a user-friendly approach where you can simply drag and drop elements to create your model, which is a convenient and effective idea."
"It is very fast to develop solutions."
"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."
"The most valuable feature is the data wrangling, which is what I mainly use it for."
"It is a stable solution...It is a scalable solution."
"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 feature is the decision tree creation."
"The setup is straightforward. Deployment doesn't take more than 30 minutes."
"Good data management and analytics."
"The most valuable feature is that you can use multiple algorithms for creating models and then you can compare the results between them."
"The technical support is very good."
"The solution is able to handle quite large amounts of data beautifully."
"The solution is very good for data mining or any mining issues."
"I like the way the product visually shows the data pipeline."
"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 is not scalable."
"It could be easier to use."
"Compared to the other data tools on the market, the user interface can be improved."
"It's very general in terms of architecture, and as a result, it doesn't support efficient running. That is, the speed needs to be improved."
"The pricing needs improvement."
"The ability to handle large amounts of data and performance in processing need to be improved."
"The most difficult part of the solution revolves around its areas concerning machine learning and deep learning."
"While I don't personally need tutorials, I can't say that it wouldn't be helpful for others to have some to help them navigate and operate the system."
"The solution is very stable, but we do have some problems with discrepancies involving SAS not matching with the latest Java versions. It's not stable in cases where SAS tries to run on a different version because SAS doesn't connect with the latest Java update. Once a month we need to restart systems from scratch."
"Technical support could be improved."
"The solution needs an easier interface for the user. The user experience isn't so easy for our clients."
"The solution is much more complex than other options."
"The visualization of the models is not very attractive, so the graphics should be improved."
"Virtualization could be much better."
"The user interface of the solution needs improvement. It needs to be more visual."
KNIME is ranked 1st in Data Mining with 50 reviews while SAS Enterprise Miner is ranked 6th in Data Mining with 13 reviews. KNIME is rated 8.2, while SAS Enterprise Miner is rated 7.6. 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 SAS Enterprise Miner writes "A stable product that is easy to deploy and can be used for structured and unstructured data mining". KNIME is most compared with RapidMiner, Microsoft Power BI, Alteryx, Dataiku and Weka, whereas SAS Enterprise Miner is most compared with SAS Visual Analytics, IBM SPSS Modeler, RapidMiner, Microsoft Azure Machine Learning Studio and SAS Analytics. See our KNIME vs. SAS Enterprise Miner report.
See our list of best Data Mining vendors and best Data Science Platforms vendors.
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.