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."Good data transformation."
"It allows for manipulation and automation, which has greatly reduced the amount of time required per project."
"Technical support is very helpful."
"Alteryx speeds up the time to obtain business answers/insights on data."
"It saves time on a lot projects. "
"It helps clean messy data and provides spatial analysis."
"The Alteryx designer has been the most useful feature in the solution."
"The analytics are easy."
"Google Cloud Datalab is very customizable."
"The APIs are valuable."
"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."
"We can't browse multiple files. When we deploy a solution on a gallery, let's say I have ten different files, and I have to upload them all at once. This is something that's difficult in the gallery. So case by case, I see some downsides, but often we do something alternative."
"There's a big jump in terms of pricing between license tiers. I'm not sure I understand why the price jumps are so high."
"It would be great if Alteryx could take third party tools and incorporate them."
"The interface could be improved."
"Alteryx's development environment could be improved as it requires installation locally and can't be developed in the cloud."
"Its most valuable feature lies in its functionality."
"The solution just lacks in terms of data visualization. That is why we use Tableau and Qlik in our organization. They help to pick up the slack. If data visualization was added, Alteryx would be a very good tool, and much more complete."
"The price of the solution could improve by being lower."
"Connectivity challenges for end-users, particularly when loading data, environments, and libraries, need to be addressed for an enhanced user experience."
"The product must be made more user-friendly."
"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."
Alteryx is ranked 3rd in Data Science Platforms with 74 reviews while Google Cloud Datalab is ranked 14th 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 Data Science Studio, RapidMiner and Microsoft Power BI, whereas Google Cloud Datalab is most compared with Databricks, IBM SPSS Statistics, Cloudera Data Science Workbench, IBM SPSS Modeler and FICO Decision Management. See our Alteryx vs. Google Cloud Datalab report.
See our list of best Data Science Platforms vendors.
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.