We performed a comparison between Google Cloud Datalab 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."All of the features of this product are quite good."
"The infrastructure is highly reliable and efficient, contributing to a positive experience."
"The APIs are valuable."
"Google Cloud Datalab is very customizable."
"In MLOps, when we are designing the data pipeline, the designing of the data pipeline is easy in Google Cloud."
"KNIME is easy to learn."
"Since KNIME is a no-code platform, it is easy to work with."
"One of the greatest advantages of KNIME is that it can be used by those without any coding experience. those with no coding background can use it."
"We have found KNIME valuable when it comes to its visualization."
"Valuable features include visual workflow creation, workflow variables (parameterisation), automatic caching of all intermediate data sets in the workflow, scheduling with the server."
"I would rate the stability of KNIME a ten out of ten."
"There are a lot of connectors available in KNIME."
"This solution is easy to use and especially good at data preparation and wrapping."
"We have also encountered challenges during our transition period in terms of data control and segmentation. The management of each channel and data structure as it has its own unique characteristics requires very detailed and precise control. The allocation should be appropriate and the complexity increases due to the different time zones and geographic locations of our clients. The process usually involves migrating the existing database sets to gcp and ensure data integrity is maintained. This is the only challenge that we faced while navigating the integers of the solution and honestly it was an interesting and unique experience."
"The interface should be more user-friendly."
"Connectivity challenges for end-users, particularly when loading data, environments, and libraries, need to be addressed for an enhanced user experience."
"There is room for improvement in the graphical user interface. So that the initial user would use it properly, that would be a good option."
"The product must be made more user-friendly."
"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."
"The license is quite expensive for us."
"The main issue with KNIME is that it sometimes uses too much CPU and RAM when working with large amounts of data."
"The pricing needs improvement."
"The documentation is lacking and it could be better."
"The predefined workflows could use a bit of improvement."
"Data visualization needs improvement."
"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."
Google Cloud Datalab is ranked 15th in Data Science Platforms with 5 reviews while KNIME is ranked 4th in Data Science Platforms with 50 reviews. Google Cloud Datalab is rated 7.6, while KNIME is rated 8.2. The top reviewer of Google Cloud Datalab writes "Easy to setup, stable and easy to design data pipelines". On the other hand, the top reviewer of KNIME writes "A low-code platform that reduces data mining time by linking script". Google Cloud Datalab is most compared with Databricks, IBM SPSS Statistics, Cloudera Data Science Workbench, Qlik Sense and Microsoft Azure Machine Learning Studio, whereas KNIME is most compared with RapidMiner, Microsoft Power BI, Alteryx, Dataiku Data Science Studio and Weka. See our Google Cloud Datalab vs. KNIME report.
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