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."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."
"The Cloudera Data Science Workbench is customizable and easy to use."
"We have been able to appreciate the considerable reduction in prototyping time."
"Clear view of the data at every step of ETL process enables changing the flow as needed."
"Stability is excellent. I would give it a nine out of ten."
"Since KNIME is a no-code platform, it is easy to work with."
"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."
"It is very fast to develop solutions."
"It has allowed us to easily implement advanced analytics into various processes."
"I was able to apply basic algorithms through just dragging and dropping."
"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."
"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."
"Data visualization needs improvement."
"KNIME could improve when it comes to large data markets."
"I'd like something that would make it easier to connect/parse websites, although I will fully admit that I'm not as proficient in KNIME as I would like to be, so it could be I'm just missing something."
"It could input more data acquisitions from other sources and it is difficult to combine with Python."
"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."
"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."
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Cloudera Data Science Workbench is ranked 18th 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 and SAS Enterprise Miner, whereas KNIME is most compared with RapidMiner, Microsoft Power BI, Alteryx, Dataiku and Weka.
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