We performed a comparison between Google Cloud Datalab and RapidMiner 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 APIs are valuable."
"In MLOps, when we are designing the data pipeline, the designing of the data pipeline is easy in Google Cloud."
"The infrastructure is highly reliable and efficient, contributing to a positive experience."
"Google Cloud Datalab is very customizable."
"The most valuable feature is what the product sets out to do, which is extracting information and data."
"I like not having to write all solutions from code. Being able to drag and drop controls, enables me to focus on building the best model, without needing to search for syntax errors or extra libraries."
"The most valuable feature of RapidMiner is that it is code free. It is similar to playing with Lego pieces and executing after you are finished to see the results. Additionally, it is easy to use and has interesting utilities when preparing the data. It has a utility to automatically launch a series of models and show the comparisons. When finished with the comparisons you can select the best one, and deploy it automatically."
"The documentation for this solution is very good, where each operator is explained with how to use it."
"RapidMiner is very easy to use."
"The GUI capabilities of the solution are excellent. Their Auto ML model provides for even non-coder data scientists to deploy a model."
"The data science, collaboration, and IDN are very, very strong."
"It is easy to use and has a huge community that I can rely on for help. Moreover, it is interactive."
"The product must be made 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."
"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."
"RapidMiner isn't cheap. It's a complete solution, but it's costly."
"Many things in the interface look nice, but they aren't of much use to the operator. It already has lots of variables in there."
"It would be helpful to have some tutorials on communicating with Python."
"A great product but confusing in some way with regard to the user interface and integration with other tools."
"RapidMiner would be improved with the inclusion of more machine learning algorithms for generating time-series forecasting models."
"RapidMiner can improve deep learning by enhancing the features."
"In the Mexican or Latin American market, it's kind of pricey."
"I would appreciate improvements in automation and customization options to further streamline processes."
Google Cloud Datalab is ranked 14th in Data Science Platforms with 5 reviews while RapidMiner is ranked 7th in Data Science Platforms with 19 reviews. Google Cloud Datalab is rated 7.6, while RapidMiner is rated 8.6. 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 RapidMiner writes "Offers good tutorials that make it easy to learn and use, with a powerful feature to compare machine learning algorithms". Google Cloud Datalab is most compared with Databricks, IBM SPSS Statistics, Cloudera Data Science Workbench, IBM SPSS Modeler and KNIME, whereas RapidMiner is most compared with KNIME, Alteryx, Dataiku Data Science Studio, Tableau and Microsoft Azure Machine Learning Studio. See our Google Cloud Datalab vs. RapidMiner report.
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