Compare Dataiku Data Science Studio vs. H2O.ai

Dataiku Data Science Studio is ranked 12th in Data Science Platforms with 4 reviews while H2O.ai is ranked 10th in Data Science Platforms with 7 reviews. Dataiku Data Science Studio is rated 7.6, while H2O.ai is rated 7.6. The top reviewer of Dataiku Data Science Studio writes "User interface is colorful, beautiful, and well-designed but sometimes the solution can be slow". 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". Dataiku Data Science Studio is most compared with Alteryx, KNIME and Databricks, whereas H2O.ai is most compared with KNIME, Microsoft Azure Machine Learning Studio and Dataiku Data Science Studio. See our Dataiku Data Science Studio vs. H2O.ai report.
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Most Helpful Review
Find out what your peers are saying about Dataiku Data Science Studio vs. H2O.ai and other solutions. Updated: January 2020.
389,978 professionals have used our research since 2012.
Quotes From Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros
I like the interface, which is probably my favorite part of the solution. It is really user-friendly for an IT person.The most valuable feature is the set of visual data preparation tools.The most valuable feature of this solution is that it is one tool that can do everything, and you have the ability to very easily push your design to prediction.Extremely easy to use with its GUI-based functionality and large compatibility with various data sources. Also, maintenance processes are much more automated than ever, with fewer errors.Cloud-based process run helps in not keeping the systems on while processes are running.

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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.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.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.AutoML helps in hands-free initial evaluations of efficiency/accuracy of ML algorithms.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.

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Cons
I find that it is a little slow during use. It takes more time than I would expect for operations to complete.In the next release of this solution, I would like to see deep learning better integrated into the tool and not simply an extension or plugin.The ability to have charts right from the explorer would be an improvement.Server up-time needs to be improved. Also, query engines like Spark and Hive need to be more stable.Although known for Big Data, the processing time to process 1.8 billion records was terribly slow (five days).There were stability issues: 1) SQL operations, such as partitioning, had bugs and showed wrong results. 2) Due to server downtime, scheduled processes used to fail. 3) Access to project folders was compromised (privacy issue) with wrong people getting access to confidential project folders.

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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.The model management features could be improved.It needs a drag and drop GUI like KNIME, for easy access to and visibility of workflows.Referring to bullet-3 as well, H2O DataFrame manipulation capabilities are too primitive.It lacks the data manipulation capabilities of R and Pandas DataFrames. We would kill for dplyr offloading H2O.

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Pricing and Cost Advice
The annual licensing fees are approximately €20 ($22 USD) per key for the basic version and €40 ($44 USD) per key for the version with everything.

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We have seen significant ROI where we were able to use the product in certain key projects and could automate a lot of processes. We were even able to reduce staff.

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Ranking
12th
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7,866
Comparisons
5,723
Reviews
4
Average Words per Review
493
Avg. Rating
7.5
10th
Views
6,013
Comparisons
4,112
Reviews
7
Average Words per Review
320
Avg. Rating
7.6
Top Comparisons
Compared 14% of the time.
Compared 24% of the time.
Also Known As
Dataiku DSS
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Dataiku
H2O.ai
Overview

Dataiku DSS is the collaborative data science software platform for teams of data scientists, data analysts, and engineers to explore, prototype, build, and deliver their own data products more efficiently.

H2O is a fully open source, distributed in-memory machine learning platform with linear scalability. H2O’s supports the most widely used statistical & machine learning algorithms including gradient boosted machines, generalized linear models, deep learning and more. H2O also has an industry leading AutoML functionality that automatically runs through all the algorithms and their hyperparameters to produce a leaderboard of the best models. The H2O platform is used by over 14,000 organizations globally and is extremely popular in both the R & Python communities.

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Learn more about Dataiku Data Science Studio
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Sample Customers
BGL BNP Paribas, Dentsu Aegis, Link Mobility Group, AramisAutopoder.io, Stanley Black & Decker, G5, PWC, Comcast, Cisco
Top Industries
VISITORS READING REVIEWS
Software R&D Company30%
Financial Services Firm16%
Comms Service Provider7%
Manufacturing Company5%
VISITORS READING REVIEWS
Software R&D Company40%
Comms Service Provider11%
Transportation Company10%
Manufacturing Company9%
Find out what your peers are saying about Dataiku Data Science Studio vs. H2O.ai and other solutions. Updated: January 2020.
389,978 professionals have used our research since 2012.
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