We performed a comparison between Alteryx and Google Cloud Datalab 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."Shortens the time required to start analyzing data and looking for insights, minimizing the tasks that do not add value to the business and maximizing the analysis phase."
"The analytics are easy."
"The most valuable feature is user-friendliness, as Alteryx can be used by those without any coding experience or experienced data scientists as it has the functionality to embed R and Python scripts."
"The most valuable feature of Alteryx is the intelligence suite."
"This is a drag-and-drop tool which is easy-to-use and yet can be customized by creating your own components."
"The ease-of-use allows non-technical business users to directly create their own solutions without the use of additional development resources."
"The solution has a very strong community that is involved in the product. It helps make the usage easier and helps us find answers to our questions."
"The modeling features are very good."
"The infrastructure is highly reliable and efficient, contributing to a positive experience."
"In MLOps, when we are designing the data pipeline, the designing of the data pipeline is easy in Google Cloud."
"All of the features of this product are quite good."
"Google Cloud Datalab is very customizable."
"The APIs are valuable."
"It is a little bit pricey."
"I'd like it to be easier to work with PDF."
"Lacks an open source edition which would be helpful."
"There could be a bit of improvement related to performance. Sometimes it demands a lot of resources for running it, like memory and search."
"They should work on its pricing."
"Sometimes workflows tend to queue up, and they tend to get canceled for some reason that we don't know sometimes."
"There are a few imputation techniques which they really need to include."
"The GUI interface functions but it could stand to be updated to a more modern look and feel."
"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."
"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."
"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."
Alteryx is ranked 3rd in Data Science Platforms with 74 reviews while Google Cloud Datalab is ranked 15th in Data Science Platforms with 5 reviews. Alteryx is rated 8.4, while Google Cloud Datalab is rated 7.6. The top reviewer of Alteryx writes "Feature-rich ETL that condenses a number of functions into one tool". On the other hand, the top reviewer of Google Cloud Datalab writes "Easy to setup, stable and easy to design data pipelines". Alteryx is most compared with KNIME, Databricks, Dataiku, RapidMiner and Microsoft Power BI, whereas Google Cloud Datalab is most compared with Databricks, IBM SPSS Statistics, Cloudera Data Science Workbench and KNIME. See our Alteryx vs. Google Cloud Datalab report.
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