We performed a comparison between IBM Watson Studio and KNIME based on real PeerSpot user reviews.
Find out in this report how the two Data Science Platforms solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."It has a lot of data connectors, which is extremely helpful."
"The solution is very easy to use."
"The system's ability to take a look at data, segment it and then use that data very differently."
"It is a stable, reliable product."
"It has greatly improved the performance because it is standardized across the company."
"The scalability of IBM Watson Studio is great."
"Watson Studio is very stable."
"For me, the valuable feature of the solution is the one that I used, which was Jupyter notebooks."
"From a user-friendliness perspective, it's a great tool."
"What I like most about KNIME is that it's user-friendly. It's a low-code, no-code tool, so students don't need coding knowledge. You can make use of different kinds of nodes. KNIME even has a good description of each node."
"It provides very fast problem solving and I don't need to do much coding in it. I just drag and drop."
"The most useful features are the readily available extensions that speed up the work."
"This solution is easy to use and it can be used to create any kind of model."
"There are a lot of connectors available in KNIME."
"Valuable features include visual workflow creation, workflow variables (parameterisation), automatic caching of all intermediate data sets in the workflow, scheduling with the server."
"We can deploy the solution in a cluster as well."
"The initial setup was complex."
"Initially, it was quite complex. For us, it was not only a matter of getting it installed, that was just a start. It was also trying to come up with a standard way of implementing it across the entire organization, which had been a challenge."
"We would like to see it less as one big, massive product, but more based on smaller services that we can then roll out to consumers."
"It's sometimes easy to get lost given the number of images the solution opens up when you click on the mouse and the amount of different tabs."
"I think maybe the support is an area where it lacks."
"The decision making in their decision making feature is less good than other options."
"So a better user interface could be very helpful"
"Watson Studio would be improved with a clearer path for the deployment of docker images."
"It needs more examples, use cases, and MOOC to learn, especially with respect to the algorithms and how to practically create a flow from end-to-end."
"I've had some problems integrating KNIME with other solutions."
"Compared to the other data tools on the market, the user interface can be improved."
"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."
"I would like it to have data visualitation capabilities. Today I'm still creating my own data visualtions tools to present my reports."
"It could be easier to use."
"The pricing needs improvement."
"The documentation is lacking and it could be better."
IBM Watson Studio is ranked 11th in Data Science Platforms with 13 reviews while KNIME is ranked 4th in Data Science Platforms with 50 reviews. IBM Watson Studio is rated 8.2, while KNIME is rated 8.2. The top reviewer of IBM Watson Studio writes "A highly robust and well-documented platform that simplifies the complex world of AI". On the other hand, the top reviewer of KNIME writes "A low-code platform that reduces data mining time by linking script". IBM Watson Studio is most compared with Databricks, Azure OpenAI, Microsoft Azure Machine Learning Studio, Google Vertex AI and Amazon Comprehend, whereas KNIME is most compared with RapidMiner, Microsoft Power BI, Alteryx, Dataiku and Weka. See our IBM Watson Studio vs. KNIME report.
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We monitor all Data Science Platforms 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.
