Compare Databricks vs. H2O.ai

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Databricks Logo
18,728 views|16,063 comparisons
H2O.ai Logo
7,596 views|5,013 comparisons
Most Helpful Review
Find out what your peers are saying about Databricks vs. H2O.ai and other solutions. Updated: November 2020.
447,439 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
"We are completely satisfied with the ease of connecting to different sources of data or pocket files in the search""Automation with Databricks is very easy when using the API.""Databricks is based on a Spark cluster and it is fast. Performance-wise, it is great.""The most valuable aspect of the solution is its notebook. It's quite convenient to use, both terms of the research and the development and also the final deployment, I can just declare the spark jobs by the load tables. It's quite convenient.""I work in the data science field and I found Databricks to be very useful.""The time travel feature is the solution's most valuable aspect.""I haven't heard about any major stability issues. At this time I feel like it's stable.""Imageflow is a visual tool that helps make it easier for business people to understand complex workflows."

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"It is helpful, intuitive, and easy to use. The learning curve is not too steep.""The ease of use in connecting to our cluster machines.""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 most valuable features are the machine learning tools, the support for Jupyter Notebooks, and the collaboration that allows you to share it across people."

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Cons
"The integration features could be more interesting, more involved.""Some of the error messages that we receive are too vague, saying things like "unknown exception", and these should be improved to make it easier for developers to debug problems.""It should have more compatible and more advanced visualization and machine learning libraries.""The solution could be improved by integrating it with data packets. Right now, the load tables provide a function, like team collaboration. Still, it's unclear as to if there's a function to create different branches and/or more branches. Our team had used data packets before, however, I feel it's difficult to integrate the current with the previous data packets.""It would be very helpful if Databricks could integrate with platforms in addition to Azure.""Databricks is an analytics platform. It should offer more data science. It should have more features for data scientists to work with.""Pricing is one of the things that could be improved.""The product needs samples and templates to help invite users to see results and understand what the product can do."

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"The model management features could be improved.""I would like to see more features related to deployment.""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.""On the topic of model training and model governance, this solution cannot handle ten or twelve models running at the same time."

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Pricing and Cost Advice
"Whenever we want to find the actual costing, we have to send an email to Databricks, so having the information available on the internet would be helpful.""I do not exactly know the costs, but one of our clients pays between $100 USD and $200 USD monthly.""Licensing on site I would counsel against, as on-site hardware issues tend to really delay and slow down delivery.""We find Databricks to be very expensive, although this improved when we found out how to shut it down at night.""The pricing depends on the usage itself."

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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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Questions from the Community
Top Answer: The most valuable feature is the ability to use SQL directly with Databricks.
Top Answer: We find Databricks to be very expensive, although this improved when we found out how to shut it down at night.
Top Answer: I have seen better user interfaces, so that is something that can be improved. It was quite hard to deploy.
Top Answer: 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.
Top Answer: On the topic of model training and model governance, this solution cannot handle ten or twelve models running at the same time. It becomes a problem. I would like to see better integration with Python… more »
Top Answer: I am a solution architect and a consultant, and I use H2O as a machine learning platform. I create ensemble models using R and H2O, tune the hyperparameters, and then deploy them. There are various… more »
Ranking
2nd
Views
18,728
Comparisons
16,063
Reviews
14
Average Words per Review
587
Avg. Rating
8.1
12th
Views
7,596
Comparisons
5,013
Reviews
5
Average Words per Review
350
Avg. Rating
7.6
Popular Comparisons
Compared 14% of the time.
Compared 8% of the time.
Compared 2% of the time.
Compared 19% of the time.
Compared 15% of the time.
Compared 6% of the time.
Also Known As
Databricks Unified Analytics, Databricks Unified Analytics Platform, Redash
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Databricks
H2O.ai
Overview

Databricks creates a Unified Analytics Platform that accelerates innovation by unifying data science, engineering, and business. It utilizes Apache Spark to help clients with cloud-based big data processing. It puts Spark on “autopilot” to significantly reduce operational complexity and management cost. The Databricks I/O module (DBIO) improves the read and write performance of Apache Spark in the cloud. An increase in productivity is ensured through Databricks’ collaborative workplace.

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 H2O.ai
Sample Customers
Elsevier, MyFitnessPal, Sharethrough, Automatic Labs, Celtra, Radius Intelligence, Yeswarepoder.io, Stanley Black & Decker, G5, PWC, Comcast, Cisco
Top Industries
VISITORS READING REVIEWS
Computer Software Company34%
Comms Service Provider12%
Media Company9%
Government5%
VISITORS READING REVIEWS
Computer Software Company33%
Comms Service Provider14%
Media Company7%
Insurance Company7%
Company Size
REVIEWERS
Small Business7%
Midsize Enterprise27%
Large Enterprise67%
REVIEWERS
Small Business13%
Midsize Enterprise25%
Large Enterprise63%
Find out what your peers are saying about Databricks vs. H2O.ai and other solutions. Updated: November 2020.
447,439 professionals have used our research since 2012.
Databricks is ranked 2nd in Data Science Platforms with 15 reviews while H2O.ai is ranked 12th in Data Science Platforms with 5 reviews. Databricks is rated 8.0, while H2O.ai is rated 7.6. The top reviewer of Databricks writes "Has a good feature set but it needs samples and templates to help invite users to see results". 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". Databricks is most compared with Amazon SageMaker, Microsoft Azure Machine Learning Studio, Azure Stream Analytics, Alteryx and Cloudera DataFlow, whereas H2O.ai is most compared with KNIME, Dataiku Data Science Studio, Amazon SageMaker, Microsoft Azure Machine Learning Studio and RapidMiner. See our Databricks vs. H2O.ai report.

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