Databricks Lakehouse Platform vs H2O

Likelihood to Recommend

Databricks has helped my teams write PySpark and Spark SQL jobs and test them out before formally integrating them in Spark jobs. Through Databricks we can create parquet and JSON output files. Datamodelers and scientists who are not very good with coding can get good insight into the data using the notebooks that can be developed by the engineers.

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Verified User

Team Lead in Engineering

Financial Services Company, 10,001+ employees

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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.

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Verified User

Contributor in Engineering

Aviation & Aerospace Company, 10,001+ employees

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Feature Rating Comparison

Databricks Lakehouse Platform

H2O

Connect to Multiple Data Sources

Automatic Data Format Detection

Databricks Lakehouse Platform

H2O

Visualization

Interactive Data Analysis

Databricks Lakehouse Platform

H2O

Interactive Data Cleaning and Enrichment

Data Transformations

Built-in Processors

Databricks Lakehouse Platform

H2O

Multiple Model Development Languages and Tools

Automated Machine Learning

Single platform for multiple model development

Self-Service Model Delivery

Databricks Lakehouse Platform

H2O

Flexible Model Publishing Options

Security, Governance, and Cost Controls

Pros

It supports all data science programming languages like Python and R
it takes very few minutes to deploy models into production
it has tools that ensures collaborations between developers

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Kofi Joshua

Machine Learning And Deep Learning Mentee

Runmila AI InstituteEducation Management, 201-500 employees

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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

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Viktor Mulac

Senior Consultant

A.T. KearneyManagement Consulting, 1001-5000 employees

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Cons

Better Localized Testing
When they were primarily OSS Spark; it was easier to test/manage releases versus the newer DB Runtime. Wish there was more configuration in Runtime less pick a version.
Graphing Support went non-existent; when it was one of their compelling general engine.

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Verified User

Director in Information Technology

Financial Services Company, 201-500 employees

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Better documentation
Improve the Visual presentations including charting etc

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Verified User

Vice-President in Information Technology

Broadcast Media Company, 51-200 employees

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Usability

Databricks Lakehouse Platform 9.0

Based on 1 answer

This has been very useful in my organization for shared notebooks, integrated data pipeline automation and data sources integrations. Integration with AWS is seamless. Non tech users can easily learn how to use Databricks. You can have your company LDAP connect to it for login based access controls to some extent

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Verified User

Team Lead in Engineering

Financial Services Company, 10,001+ employees

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No score

No answers yet

No answers on this topic

Support Rating

No score

No answers yet

No answers on this topic

H2O 9.0

Based on 1 answer

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

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Verified User

Contributor in Engineering

Aviation & Aerospace Company, 10,001+ employees

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Alternatives Considered

I also use Microsoft Azure Machine Learning in parallel with Databricks. They use different file formats which teach me to be flexible and able to write different programs. They are equally useful to me and I would like to master both platforms for any future usage. I do prefer Databricks because it could be free if I decided to go with the Databricks Community Edition only.

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Ann Le

Freelance Translator

ZOO Digital Group plcEntertainment, 501-1000 employees

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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.

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Viktor Mulac

Senior Consultant

A.T. KearneyManagement Consulting, 1001-5000 employees

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Return on Investment

Rapid growth of analytics within our company.
Cost model aligns with usage allowing us to make a reasonable initial investment and scale the cost as we realize the value.
Platform is easy to learn and Databricks provides excellent support and training.
Platform does not require a large DevOPs investment

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Verified User

Strategist in Engineering

Computer Hardware Company, 10,001+ employees

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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

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Verified User

Vice-President in Information Technology

Broadcast Media Company, 51-200 employees

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Pricing Details

General

Free Trial

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Free/Freemium Version

—

Premium Consulting/Integration Services

—

Entry-level set up fee?

No

Databricks Lakehouse Platform Editions & Modules

Edition

Standard	$0.07¹
Premium	$0.10¹
Enterprise	$0.13¹

Per DBU

Additional Pricing Details

—

General

Free Trial

—

Free/Freemium Version

—

Premium Consulting/Integration Services

—

Entry-level set up fee?

No

H2O Editions & Modules

—

Additional Pricing Details

—

Rating Summary

Databricks Lakehouse Platform

H2O

Databricks Lakehouse Platform

H2O

Databricks Lakehouse Platform

H2O

Likelihood to Recommend