Likelihood to Recommend Most suited if in little time you wanted to build and train a model. Then, H2O makes life very simple. It has support with R, Python and Java, so no programming dependency is required to use it. It's very simple to use. If you want to modify or tweak your ML algorithm then H2O is not suitable. You can't develop a model from scratch.
Read full review Paxata can be highly useful to someone who doesn't like/have any experience with writing codes to treat data before using it as input into BI dashboards. Paxata can accelerate data cleaning in environments where a large amount of unclean data is generated and business decisions on the go are required. It performs really well while dealing with natural language.
Read full review Pros Excellent analytical and prediction tool In the beginning, usage of H20 Flow in Web UI enables quick development and sharing of the analytical model Readily available algorithms, easy to use in your analytical projects Faster than Python scikit learn (in machine learning supervised learning area) It can be accessed (run) from Python, not only JAVA etc. Well documented and suitable for fast training or self studying In the beginning, one can use the clickable Flow interface (WEB UI) and later move to a Python console. There is then no need to click in H20 Flow It can be used as open source Read full review Visualize distributions in large data sets effectively which enable the user to quickly spot outliers and treat them appropriately Provides recommendation to merge datasets based on matching column values The cluster and edit feature in my opinion is its most powerful feature and reduces cardinality in column with text Read full review Cons Better documentation Improve the Visual presentations including charting etc Read full review Doesn't provide recommendation on how to impute values There is a lag quite often We can say whether a column has errors or quality issues in the first look Read full review Support Rating The overall experience I have with H2O is really awesome, even with its cost effectiveness.
Read full review Alternatives Considered Both are open source (though H2O only up to some level). Both comprise of deep learning, but H2O is not focused directly on deep learning, while Tensor Flow has a "laser" focus on deep learning. H2O is also more focused on scalability. H2O should be looked at not as a competitor but rather a complementary tool. The use case is usually not only about the algorithms, but also about the data model and data logistics and accessibility. H2O is more accessible due to its UI. Also, both can be accessed from Python. The community around
TensorFlow seems larger than that of H2O.
Read full review Paxata is a much better tool when it comes to handling natural language but
Talend provides recommendations on how to impute missing values and outliers. Paxata provides recommendations on dataset tie-ups and joins but
Talend doesn't provide any such recommendations. In paxata you can visualize distribution of data in a column and filter them by dragging and selecting the section you'd like to retain
Read full review Return on Investment Positive impact: saving in infrastructure expenses - compared to other bulky tools this costs a fraction Positive impact: ability to get quick fixes from H2O when problems arise - compared to waiting for several months/years for new releases from other vendors Positive impact: Access to H2O core team and able to get features that are needed for our business quickly added to the core H2O product Read full review It saves time to clean data It reduces the requirement of too many data engineer/stewards and hence adds positive impact on the return of the business Read full review ScreenShots