We performed a comparison between KNIME and Pentaho Business Analytics based on real PeerSpot user reviews.
Find out what your peers are saying about Knime, Weka, IBM and others in Data Mining."We have found KNIME valuable when it comes to its visualization."
"It is very fast to develop solutions."
"I would rate the stability of KNIME a ten out of ten."
"We can deploy the solution in a cluster as well."
"Since KNIME is a no-code platform, it is easy to work with."
"The most valuable is the ability to seamlessly connect operators without the need for extensive programming."
"Stability is excellent. I would give it a nine out of ten."
"We have been able to appreciate the considerable reduction in prototyping time."
"The most valuable feature of Pentaho is the Tableau report."
"Easy to use components to create the job."
"Pentaho Business Analytics' best features include the ease of developing data flows and the wide range of options to connect to databases, including those on the cloud."
"I use the BI Server, CDE Dashboards, Saiku, and Kettle, because these tools are very good and highly experienced."
"We were able to install it without any assistance from tech support."
"Pentaho is an analytics platform that can be used when an organization has a lot of big data storage systems already installed and needs to manage and analyze that data. It has a specific use case for unstructured data, such as documents, and needs to be able to search and analyze it."
"The initial setup is pretty straightforward."
"It could input more data acquisitions from other sources and it is difficult to combine with Python."
"It could be easier to use."
"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 program is not fit for handling very large files or databases (greater than 1GB); it gets too slow and has a tendency to crash easily."
"The visualization functionalities are not good (cannot be compared to, for instance, the possibilities in R)."
"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."
"KNIME needs to provide more documentation and training materials, including webinars or online seminars."
"KNIME can improve by adding more automation tools in the query, similar to UiPath or Blue Prism. It would make the data collection and cleanup duties more versatile."
"Pentaho, at the general level, should greatly improve the easy construction of its dashboards and easy integration of information from different sources without technical user intervention."
"Version control would be a good addition."
"Logging capability is needed."
"Another concern is that Pentaho is not customizable or interactive."
"The repository should be improved."
"Pentaho Business Analytics' user interface is outdated."
"Deployment is not simple. It is not simple because we are dealing with a lot of data; we are dealing with a lot of storage. So, it's not a simple process."
"We did not achieve the ROI. The work delivered to users had lesser value than the subscription cost."
KNIME is ranked 1st in Data Mining with 50 reviews while Pentaho Business Analytics is ranked 19th in BI (Business Intelligence) Tools with 42 reviews. KNIME is rated 8.2, while Pentaho Business Analytics is rated 8.0. The top reviewer of KNIME writes "A low-code platform that reduces data mining time by linking script". On the other hand, the top reviewer of Pentaho Business Analytics writes "Flexible, easy to understand, and simple to set up". KNIME is most compared with RapidMiner, Microsoft Power BI, Alteryx, Dataiku and Domino Data Science Platform, whereas Pentaho Business Analytics is most compared with Microsoft Power BI, Databricks, SAP Crystal Reports, Microsoft SQL Server Reporting Services and Tableau.
We monitor all Data Mining 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.