We performed a comparison between Alteryx and H2O.ai based on real PeerSpot user reviews.
Find out what your peers are saying about Databricks, Microsoft, Alteryx and others in Data Science Platforms."I like that I can merge data from different sources into one place."
"It allows for manipulation and automation, which has greatly reduced the amount of time required per project."
"Good data transformation."
"It's super easy to learn how to use it — the learning curve is very small."
"It saves time on a lot projects. "
"Alteryx's connectivity is essential. We like the ability to connect the solution to multiple sources. It's easier than other data modeling and extraction solutions. It's built on a self-service concept, so it's easy for anyone to open the tool and directly import or export data from it."
"The scheduling within the solution is excellent."
"Geo features have made spatial mapping large retail universes possible."
"One of the most interesting features of the product is their driverless component. The driverless component allows you to test several different algorithms along with navigating you through choosing the best algorithm."
"AutoML helps in hands-free initial evaluations of efficiency/accuracy of ML algorithms."
"The most valuable features are the machine learning tools, the support for Jupyter Notebooks, and the collaboration that allows you to share it across people."
"Fast training, memory-efficient DataFrame manipulation, well-documented, easy-to-use algorithms, ability to integrate with enterprise Java apps (through POJO/MOJO) are the main reasons why we switched from Spark to H2O."
"The ease of use in connecting to our cluster machines."
"It is helpful, intuitive, and easy to use. The learning curve is not too steep."
"The solution is improving continuously. They have, for example, just added automatic insights. If they continue to improve on their overall service offering, that would be ideal."
"More statistics tools: We can use to compare SPSS statistics with some automated advisory."
"The principal problem is the pricing. They're expensive products."
"The solution can be quite complex in some aspects."
"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."
"Configuration is very low."
"It seems to me that it is not always user friendly."
"They should work on its pricing."
"It lacks the data manipulation capabilities of R and Pandas DataFrames. We would kill for dplyr offloading H2O."
"The model management features could be improved."
"Referring to bullet-3 as well, H2O DataFrame manipulation capabilities are too primitive."
"It needs a drag and drop GUI like KNIME, for easy access to and visibility of workflows."
"On the topic of model training and model governance, this solution cannot handle ten or twelve models running at the same time."
"The interpretability module has room for improvement. Also, it needs to improve its ability to integrate with other systems, like SageMaker, and the overall integration capability."
"I would like to see more features related to deployment."
Earn 20 points
Alteryx is ranked 3rd in Data Science Platforms with 74 reviews while H2O.ai is ranked 19th in Data Science Platforms. Alteryx is rated 8.4, while H2O.ai 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 H2O.ai writes "It is helpful, intuitive, and easy to use. The learning curve is not too steep". Alteryx is most compared with KNIME, Databricks, Dataiku Data Science Studio, RapidMiner and Qlik Sense, whereas H2O.ai is most compared with Databricks, Amazon SageMaker, Dataiku Data Science Studio, Microsoft Azure Machine Learning Studio and SAS Visual Analytics.
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