We performed a comparison between Google Cloud Datalab and SAS Enterprise Miner 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."Google Cloud Datalab is very customizable."
"The infrastructure is highly reliable and efficient, contributing to a positive experience."
"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."
"I found the ease of use of the solution the most valuable. Additionally, other valuable features include: the user interface, power to extract data, compatibility with other technologies (specifically with PS400), and automation of several tasks."
"The solution is able to handle quite large amounts of data beautifully."
"he solution is scalable."
"The most valuable feature is that you can use multiple algorithms for creating models and then you can compare the results between them."
"I like the way the product visually shows the data pipeline."
"Most of the features, especially on the data analysis tool pack, are really good. The way they do clustering and output is great. You can do fairly elaborate outputs. The results, the ensembles, all of these, are fantastic."
"Good data management and analytics."
"The setup is straightforward. Deployment doesn't take more than 30 minutes."
"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 interface should be more user-friendly."
"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 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."
"The product must provide better integration with cloud-native technologies."
"While I don't personally need tutorials, I can't say that it wouldn't be helpful for others to have some to help them navigate and operate the system."
"The visualization of the models is not very attractive, so the graphics should be improved."
"The initial setup is challenging if doing it for the first time."
"The user interface of the solution needs improvement. It needs to be more visual."
"The solution needs an easier interface for the user. The user experience isn't so easy for our clients."
"Virtualization could be much better."
"Technical support could be improved."
Google Cloud Datalab is ranked 14th in Data Science Platforms with 5 reviews while SAS Enterprise Miner is ranked 15th in Data Science Platforms with 13 reviews. Google Cloud Datalab is rated 7.6, while SAS Enterprise Miner is rated 7.6. 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 SAS Enterprise Miner writes "A stable product that is easy to deploy and can be used for structured and unstructured data mining". Google Cloud Datalab is most compared with Databricks, IBM SPSS Statistics, Cloudera Data Science Workbench, IBM SPSS Modeler and KNIME, whereas SAS Enterprise Miner is most compared with SAS Visual Analytics, IBM SPSS Modeler, RapidMiner, Microsoft Azure Machine Learning Studio and SAS Analytics. See our Google Cloud Datalab vs. SAS Enterprise Miner 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.
