Collibra Platform vs. H2O.ai

Collibra is well suited where you have multiple reporting environments and multiple source systems. Collibra works well in our environment because we can delegate roles and administration to departments or it can be used in a centralized environment. The ability to customize attributes and assets as well and integrate using the rest api is very important to us.

Incentivized

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.

Incentivized

• Data Lineage and traceability
• Easy to customize
• Easy tool to use for business users
• Workflow
• Out of the box metrics

Incentivized

• 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

Incentivized

• Data Quality and Data Profiling
• Mobile interface

Incentivized

• Better documentation
• Improve the Visual presentations including charting etc

Incentivized

The overall experience I have with H2O is really awesome, even with its cost effectiveness.

Incentivized

Collibra offers more features [than alternatives] and is easy for business users to use. We did a proof of concept with Collibra using the Cloud service that allowed us to kick the tires and get a comfort level before we made the investment. At the time of our purchase the market was very immature and is continuing to evolve.

Incentivized

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.

Incentivized

• ROI is hard to measure because there are so many soft benefits that come from the tool. We do have a variety of metrics that we track using Collibra to better gauge the maturity of data governance.

Incentivized

• 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

Incentivized