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Data Science Workbench

Data Science Workbench

Overview

What is Data Science Workbench?

Cloudera Data Science Workbench enables secure self-service data science for the enterprise. It is a collaborative environment where developers can work with a variety of libraries and frameworks.

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Recent Reviews

Cloudera review

6 out of 10
September 15, 2019
Incentivized
Cloudera is being used on a 6-node Hadoop cluster used for sandbox demonstrations and development. The business problem it was selected to …
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Pricing

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Data Science Workbench

Data Science Workbench

N/A
Unavailable

What is Data Science Workbench?

Cloudera Data Science Workbench enables secure self-service data science for the enterprise. It is a collaborative environment where developers can work with a variety of libraries and frameworks.

Entry-level set up fee?

  • No setup fee

Offerings

  • Free Trial
  • Free/Freemium Version
  • Premium Consulting/Integration Services

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Product Demos

Demo de Cloudera Data Science Workbench con vuelos comerciales en R

YouTube

CoolTalks 2021 - Machine Learning and Data Visualisation with Cloudera​

YouTube

Cloudera Data Science Workbench 1.4 Accelerates Everyday Workflows for Data Scientists

YouTube
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Product Details

What is Data Science Workbench?

Cloudera Data Science Workbench enables secure self-service data science for the enterprise. It is a collaborative environment where developers can work with a variety of libraries and frameworks.

Data Science Workbench Technical Details

Operating SystemsUnspecified
Mobile ApplicationNo
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Comparisons

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Reviews and Ratings

(14)

Attribute Ratings

Reviews

(1-1 of 1)
Companies can't remove reviews or game the system. Here's why
September 15, 2019

Cloudera review

Verified User
Professional in Research & Development
Utilities Company, 10,001+ employees
Score 6 out of 10
Vetted Review
Verified User
Incentivized
Use Cases and Deployment Scope
Cloudera is being used on a 6-node Hadoop cluster used for sandbox demonstrations and development. The business problem it was selected to address was the ability to create Machine Learning models in an enterprise environment based on data lake architecture.
Pros and Cons
  • The ability to use multiple languages.
  • GitHub integration.
  • Scalable.
  • Installation is difficult.
  • Upgrades are difficult.
  • Licensing options are not flexible.
Likelihood to Recommend
The use cases are specific to my industry, and we’re implemented for experimentation and scoring of predictive models.
Data Science Workbench Feature Ratings
Platform Connectivity (4)
70%
7.0
Connect to Multiple Data Sources
60%
6.0
Extend Existing Data Sources
70%
7.0
Automatic Data Format Detection
70%
7.0
MDM Integration
80%
8.0
Data Exploration (2)
90%
9.0
Visualization
90%
9.0
Interactive Data Analysis
90%
9.0
Data Preparation (4)
77.5%
7.8
Interactive Data Cleaning and Enrichment
80%
8.0
Data Transformations
80%
8.0
Data Encryption
80%
8.0
Built-in Processors
70%
7.0
Platform Data Modeling (3)
96.66666666666666%
9.7
Multiple Model Development Languages and Tools
90%
9.0
Single platform for multiple model development
100%
10.0
Self-Service Model Delivery
100%
10.0
Model Deployment (2)
70%
7.0
Flexible Model Publishing Options
100%
10.0
Security, Governance, and Cost Controls
40%
4.0
Return on Investment
  • Paid off for demonstration purposes.
Support Rating
5
It is expensive and difficult to install and maintain.
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