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Cloudera Data Science Workbench

Score6.7 out of 10

13 Reviews and Ratings

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What is Cloudera 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.

Categories & Use Cases

Key Features

Learn about the best (and worst!) features Cloudera Data Science Workbench has to offer, as determined by TrustRadius' reviewers.
Based on 13 ratings of Cloudera Data Science Workbench's features
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Top Performing Features

  • Self-Service Model Delivery

    Multiple model delivery modes to comply with existing workflows

    Category average: 8.3

  • Flexible Model Publishing Options

    Publish models as REST APIs, hosted interactive web apps or as scheduled jobs for generating reports or running ETL tasks.

    Category average: 9.2

  • Extend Existing Data Sources

    Use R or Python to create custom connectors for any APIs or databases

    Category average: 8.9

Areas for Improvement

  • Automatic Data Format Detection

    Automatic detection of data formats and schemas

    Category average: 9.2

  • Interactive Data Cleaning and Enrichment

    Access to visual processors for data wrangling

    Category average: 9

  • Automated Machine Learning

    Tools to help automate algorithm development

    Category average: 8.9

Reviews

What users are saying about Cloudera Data Science Workbench.
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Exciting tool from Cloudera

Incentivized
Bharadwaj (Brad) ChivukulaView profile
Sr.Technical Manager/Delivery Manager in Information Technology at Nisum Technologies, Inc. (10,001+ employees employees)

Pros

  • 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

Cons

  • Not as great as RStudio; lacks some features when compared with it
  • It is quite simple still (because its very early in its initiative), and companies may want to wait until they see a more developed product

Return on Investment

  • As the tool itself can access all the HDFS, Spark data easily, the wait time between teams has reduced
  • Installation was a breeze, and ramp up time was fairly easy

Alternatives Considered

Microsoft Azure Machine Learning Workbench

Other Software Used

Hadoop, HBase, Apache Solr

The perfect analytics and data science platform for your Cloudera Data Platform

Incentivized
Verified User
Professional in Information Technology (1001-5000 employees employees)

Pros

  • Enterprise grade security.
  • Self-service analytics platform.
  • Popular programming support.

Cons

  • Lacks features offered by competition.
  • Limited license scheme options.
  • Installation in production can be challenging.

Return on Investment

  • Reducing cost by eliminating silos.
  • Faster adoption and time to market.
  • Added cost for enterprise license.

Alternatives Considered

Azure Data Science Virtual Machines (DSVM)

Other Software Used

Experian PowerCurve Strategy Management, Experian Aperture Data Studio, Experian Pandora

Cloudera review

Incentivized
Verified User
Professional in Research & Development (10,001+ employees employees)

Pros

  • The ability to use multiple languages.
  • GitHub integration.
  • Scalable.

Cons

  • Installation is difficult.
  • Upgrades are difficult.
  • Licensing options are not flexible.

Return on Investment

  • Paid off for demonstration purposes.