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."I like the interface, which is probably my favorite part of the solution. It is really user-friendly for an IT person."
"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 solution is quite stable."
"Cloud-based process run helps in not keeping the systems on while processes are running."
"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."
"Data Science Studio's data science model is very useful."
"The most valuable feature is the set of visual data preparation tools."
"Automation is most valuable. It allows me to automatically download information from different sources, and once I create a workflow, I can apply it anytime I want. So, there is efficiency at the same 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 most valuable features of KNIME are its ability to convert your sub-workflow into a node. For example, the workflow has many individual native nodes that can be converted into a single node. This representation has simplified my workflow to a great extent. I can present my workflow in a very compact way."
"This open-source product can compete with category leaders in ELT software."
"We leverage KNIME flexibility in order to query data from our database and manipulate them for any ad-hoc business case, before presenting results to stakeholders."
"KNIME is quite scalable, which is one of the most important features that we found."
"All of the features related to the ETL are fantastic. That includes the connectors to other programs, databases, and the meta node function."
"It's very convenient to write your own algorithms in KNIME. You can write it in Java script or Python transcript."
"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."
"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."
"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."
"I think it would help if Data Science Studio added some more features and improved the data model."
"Although known for Big Data, the processing time to process 1.8 billion records was terribly slow (five days)."
"Dataiku still needs some coding, and that could be a difference where business data scientists would go for DataRobot more than Dataiku."
"The ability to have charts right from the explorer would be an improvement."
"Server up-time needs to be improved. Also, query engines like Spark and Hive need to be more stable."
"The predefined workflows could use a bit of improvement."
"One area that could be improved is increasing awareness and adoption of KNIME among organizations. Despite its capabilities, it is not as well-known as other tools. The advertising and marketing efforts to reach out to companies and universities have not been very successful."
"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."
"When deploying models on a regular system, it works fine. However, when accuracy is a priority, hyperparameter tuning is necessary. Currently, KNIME doesn't have the best tools for this which they could improve in this area."
"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."
"They could add more detailed examples of the functionality of every node, how it works and how we can use it, to make things easier at the beginning."
"The documentation needs a proper rework. "
"KNIME is not scalable."
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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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.
