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."
"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."
"Data Science Studio's data science model is very useful."
"The most valuable feature is the set of visual data preparation tools."
"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."
"I like the interface, which is probably my favorite part of the solution. It is really user-friendly for an IT person."
"Usability, and organising workflows in very neat manner. Controlling workflow through variables is something amazing."
"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."
"Since KNIME is a no-code platform, it is easy to work with."
"The product is open-source and therefore free to use."
"We are able to automate several functions which were done manually. I can integrate several data sets quickly and easily, to support analytics."
"It is a stable solution...It is a scalable solution."
"It can handle an unlimited amount of data, which is the advantage of using Knime."
"We have found KNIME valuable when it comes to its visualization."
"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."
"Although known for Big Data, the processing time to process 1.8 billion records was terribly slow (five days)."
"I find that it is a little slow during use. It takes more time than I would expect for operations to complete."
"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."
"Server up-time needs to be improved. Also, query engines like Spark and Hive need to be more stable."
"I think it would help if Data Science Studio added some more features and improved the data model."
"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."
"I would like it to have data visualitation capabilities. Today I'm still creating my own data visualtions tools to present my reports."
"The main issue with KNIME is that it sometimes uses too much CPU and RAM when working with large amounts of data."
"There are a lot of tools in the product and it would help if they were grouped into classes where you can select a function, rather than a specific tool."
"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."
"In my environment, I need to access a lot of servers with different characteristics and access methods. Some of my servers have to be accessed using proxy which is not supported by KNIME, so I still need to create the middleware to supply the source of my KNIME configurations."
"The ability to handle large amounts of data and performance in processing need to be improved."
"The license is quite expensive for us."
"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."
Dataiku is ranked 11th 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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