What users are saying about
9 Ratings
14 Ratings
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Score 7.3 out of 100
9 Ratings
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Score 8.7 out of 100

Feature Set Ratings

  • H2O ranks higher in 5 feature sets: Platform Connectivity, Data Exploration, Data Preparation, Platform Data Modeling, Model Deployment

Platform Connectivity

7.5

Data Science Workbench

75%
8.0

H2O

80%
Cloudera Data Science Workbench ranks higher in 2/4 features

Connect to Multiple Data Sources

7.0
70%
2 Ratings
8.0
80%
1 Rating

Extend Existing Data Sources

8.0
80%
2 Ratings
N/A
0 Ratings

Automatic Data Format Detection

7.0
70%
2 Ratings
8.0
80%
1 Rating

MDM Integration

8.0
80%
2 Ratings
N/A
0 Ratings

Data Exploration

7.6

Data Science Workbench

76%
8.5

H2O

85%
H2O ranks higher in 2/2 features

Visualization

7.1
71%
2 Ratings
8.0
80%
1 Rating

Interactive Data Analysis

8.0
80%
2 Ratings
9.0
90%
1 Rating

Data Preparation

7.8

Data Science Workbench

78%
9.3

H2O

93%
H2O ranks higher in 3/4 features

Interactive Data Cleaning and Enrichment

7.0
70%
2 Ratings
10.0
100%
1 Rating

Data Transformations

8.0
80%
2 Ratings
9.0
90%
1 Rating

Data Encryption

8.0
80%
2 Ratings
N/A
0 Ratings

Built-in Processors

8.0
80%
2 Ratings
9.0
90%
1 Rating

Platform Data Modeling

7.6

Data Science Workbench

76%
10.0

H2O

100%
H2O ranks higher in 4/4 features

Multiple Model Development Languages and Tools

8.0
80%
2 Ratings
10.0
100%
1 Rating

Automated Machine Learning

7.0
70%
1 Rating
10.0
100%
1 Rating

Single platform for multiple model development

7.1
71%
2 Ratings
10.0
100%
1 Rating

Self-Service Model Delivery

8.1
81%
2 Ratings
10.0
100%
1 Rating

Model Deployment

8.0

Data Science Workbench

80%
9.0

H2O

90%
H2O ranks higher in 2/2 features

Flexible Model Publishing Options

8.1
81%
2 Ratings
10.0
100%
1 Rating

Security, Governance, and Cost Controls

7.8
78%
2 Ratings
8.0
80%
1 Rating

Attribute Ratings

  • Cloudera Data Science Workbench is rated higher in 1 area: Likelihood to Recommend
  • H2O is rated higher in 1 area: Support Rating

Likelihood to Recommend

8.9

Data Science Workbench

89%
3 Ratings
8.1

H2O

81%
3 Ratings

Support Rating

7.7

Data Science Workbench

77%
3 Ratings
9.0

H2O

90%
2 Ratings

Likelihood to Recommend

Data Science Workbench

Organizations which already implemented on-premise Hadoop based Cloudera Data Platform (CDH) for their Big Data warehouse architecture will definitely get more value from seamless integration of Cloudera Data Science Workbench (CDSW) with their existing CDH Platform. However, for organizations with hybrid (cloud and on-premise) data platform without prior implementation of CDH, implementing CDSW can be a challenge technically and financially.
Anonymous | TrustRadius Reviewer

H2O

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.
Anonymous | TrustRadius Reviewer

Pros

Data Science Workbench

  • One single IDE (browser based application) that makes Scala, R, Python integrated under one tool
  • For larger organizations/teams, it lets you be self reliant
  • As it sits on your cluster, it has very easy access of all the data on the HDFS
  • Linking with Github is a very good way to keep the code versions intact
Bharadwaj (Brad) Chivukula | TrustRadius Reviewer

H2O

  • 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
Viktor Mulac | TrustRadius Reviewer

Cons

Data Science Workbench

  • Installation is difficult.
  • Upgrades are difficult.
  • Licensing options are not flexible.
Anonymous | TrustRadius Reviewer

H2O

  • Better documentation
  • Improve the Visual presentations including charting etc
Anonymous | TrustRadius Reviewer

Pricing Details

Data Science Workbench

General

Free Trial
Free/Freemium Version
Premium Consulting/Integration Services
Entry-level set up fee?
No

Starting Price

H2O

General

Free Trial
Free/Freemium Version
Premium Consulting/Integration Services
Entry-level set up fee?
No

Starting Price

Support Rating

Data Science Workbench

Data Science Workbench 7.7
Based on 3 answers
Cloudera Data Science Workbench has excellence online resources support such as documentation and examples. On top of that the enterprise license also comes with SLA on opening a ticket to Cloudera Services and support for complaint handling and troubleshooting by email or through a phone call. On top of that it also offers additional paid training services.
Anonymous | TrustRadius Reviewer

H2O

H2O 9.0
Based on 2 answers
The overall experience I have with H2O is really awesome, even with its cost effectiveness.
Anonymous | TrustRadius Reviewer

Alternatives Considered

Data Science Workbench

Both the tools have similar features and have made it pretty easy to install/deploy/use. Depending on your existing platform (Cloudera vs. Azure) you need to pick the Workbench. Another observation is that Cloudera has better support where you can get feedback on your questions pretty fast (unlike MS). As its a new product, I expect MS to be more efficient in handling customers questions.
Bharadwaj (Brad) Chivukula | TrustRadius Reviewer

H2O

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.
Viktor Mulac | TrustRadius Reviewer

Return on Investment

Data Science Workbench

  • Paid off for demonstration purposes.
Anonymous | TrustRadius Reviewer

H2O

  • 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
Anonymous | TrustRadius Reviewer

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