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."Alteryx is a low-code platform, and that's the biggest reason why we chose it."
"The solution has been stable."
"The Alteryx designer has been the most useful feature in the solution."
"The most valuable feature of Alteryx is its unlimited handling capabilities."
"Data processing is most valuable. It is one of the fastest data blockers out there in the market, which is a fascinating thing about Alteryx."
"The support is very good."
"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."
"It helps clean messy data and provides spatial analysis."
"All of the features of this product are quite good."
"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."
"The APIs are valuable."
"Google Cloud Datalab is very customizable."
"The principal problem is the pricing. They're expensive products."
"There are a few hiccups with specific data sets and languages or formats that the data comes in. That may be a minor problem, but we can work through it. We had some issues looking at XML format in added data, but it wasn't significant."
"The only weakness I would say is on the visualization side of having that dynamic incapability."
"I think better visualization would be helpful to this solution."
"The gallery could improve in Alteryx. Additionally, if there was a Conditional Join feature it would be beneficial. Since I do not have this feature I have to use Python scripts."
"I think they should really work on integrating or have a capacity to integrate some algorithmic code. I think that's one of the most important things they need to be doing."
"I'd like to see more artificial intelligence business tools or features in Alteryx."
"The workflow and pipeline need to improve."
"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."
"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."
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 Data Science Studio, 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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