We performed a comparison between Cloudera Data Science Workbench and KNIME based on real PeerSpot user reviews.
Find out what your peers are saying about Databricks, Microsoft, Alteryx and others in Data Science Platforms."The Cloudera Data Science Workbench is customizable and easy to use."
"I appreciate CDSW's ability to logically segregate environments, such as data, DR, and production, ensuring they don't interfere with each other. The deployment of machine learning is fast and easy to manage. Its API calls are also fast."
"This solution is easy to use and especially good at data preparation and wrapping."
"It's very convenient to write your own algorithms in KNIME. You can write it in Java script or Python transcript."
"The product is open-source and therefore free to use."
"KNIME is easy to learn."
"Key features include: very easy-to-use visual interface; Help functions and clear explanations of the functionalities and the used algorithms; Data Wrangling and data manipulation functionalities are certainly sufficient, as well as the looping possibilities which help you to automate parts of the analysis."
"All of the features related to the ETL are fantastic. That includes the connectors to other programs, databases, and the meta node function."
"It has allowed us to easily implement advanced analytics into various processes."
"KNIME is fast and the visualization provides a lot of clarity. It clarifies your thinking because you can see what's going on with your data."
"The tool's MLOps is not good. It's pricing also needs to improve."
"Running this solution requires a minimum of 12GB to 16GB of RAM."
"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."
"Data visualization needs improvement."
"The predefined workflows could use a bit of improvement."
"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."
"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 ability to handle large amounts of data and performance in processing need to be improved."
"Both RapidMiner and KNIME should be made easier to use in the field of deep learning."
"They should look at other vendors like Alteryx that are more user friendly and modern."
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Cloudera Data Science Workbench is ranked 16th in Data Science Platforms with 2 reviews while KNIME is ranked 4th in Data Science Platforms with 50 reviews. Cloudera Data Science Workbench is rated 7.0, while KNIME is rated 8.2. The top reviewer of Cloudera Data Science Workbench writes "Useful for data science modeling but improvement is needed in MLOps and pricing ". On the other hand, the top reviewer of KNIME writes "A low-code platform that reduces data mining time by linking script". Cloudera Data Science Workbench is most compared with Databricks, Amazon SageMaker, Microsoft Azure Machine Learning Studio, Dataiku Data Science Studio and Domino Data Science Platform, whereas KNIME is most compared with RapidMiner, Microsoft Power BI, Alteryx, Weka and Dataiku Data Science Studio.
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