We performed a comparison between Dataiku 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."Cloud-based process run helps in not keeping the systems on while processes are running."
"The solution is quite stable."
"I like the interface, which is probably my favorite part of the solution. It is really user-friendly for an IT person."
"Data Science Studio's data science model is very useful."
"The most valuable feature is the set of visual data preparation tools."
"Extremely easy to use with its GUI-based functionality and large compatibility with various data sources. Also, maintenance processes are much more automated than ever, with fewer errors."
"The most valuable feature of this solution is that it is one tool that can do everything, and you have the ability to very easily push your design to prediction."
"If many teams are collaborating and sharing Jupyter notebooks, it's very useful."
"We have found KNIME valuable when it comes to its visualization."
"I know I don't use it to its full capacity, but I love the Rule Engine feature. It has allowed me to create lookup tables on the fly and break down text fields into quantifiable data."
"The solution allows for sharing model designs and model operations with other data analysts."
"Usability, and organising workflows in very neat manner. Controlling workflow through variables is something amazing."
"From a user-friendliness perspective, it's a great tool."
"It can handle an unlimited amount of data, which is the advantage of using Knime."
"We have been able to appreciate the considerable reduction in prototyping time."
"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 ability to have charts right from the explorer would be an improvement."
"In the next release of this solution, I would like to see deep learning better integrated into the tool and not simply an extension or plugin."
"Dataiku still needs some coding, and that could be a difference where business data scientists would go for DataRobot more than Dataiku."
"The interface for the web app can be a bit difficult. It needs to have better capabilities, at least for developers who like to code. This is due to the fact that everything is enabled in a single window with different tabs. For them to actually develop and do the concurrent testing that needs to be done, it takes a bit of time. That is one improvement that I would like to see - from a web app developer perspective."
"There were stability issues: 1) SQL operations, such as partitioning, had bugs and showed wrong results. 2) Due to server downtime, scheduled processes used to fail. 3) Access to project folders was compromised (privacy issue) with wrong people getting access to confidential project folders."
"I find that it is a little slow during use. It takes more time than I would expect for operations to complete."
"Although known for Big Data, the processing time to process 1.8 billion records was terribly slow (five days)."
"I think it would help if Data Science Studio added some more features and improved the data model."
"KNIME needs to provide more documentation and training materials, including webinars or online seminars."
"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."
"I would like it to have data visualitation capabilities. Today I'm still creating my own data visualtions tools to present my reports."
"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."
"I've had some problems integrating KNIME with other solutions."
"The documentation is lacking and it could be better."
"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."
"The dynamic column name feature could be improved. When attempting to automate processes involving columns, such as with companies, it becomes difficult to achieve the same result when we make changes."
Dataiku is ranked 7th in Data Science Platforms with 7 reviews while KNIME is ranked 4th in Data Science Platforms with 50 reviews. Dataiku is rated 8.2, while KNIME is rated 8.2. The top reviewer of Dataiku writes "Gives different aspects of modeling approaches and good for multiple teams' collaboration". On the other hand, the top reviewer of KNIME writes "A low-code platform that reduces data mining time by linking script". Dataiku is most compared with Databricks, Alteryx, RapidMiner, Microsoft Azure Machine Learning Studio and Amazon SageMaker, whereas KNIME is most compared with RapidMiner, Microsoft Power BI, Alteryx, Weka and Microsoft Azure Machine Learning Studio. See our Dataiku vs. KNIME report.
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