Data Science Workbench Reviews

13 Ratings
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Score 8.0 out of 100

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Reviews (1-3 of 3)

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November 18, 2020
Anonymous | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
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Cloudera Data Science Workbench (CDSW) is mainly being used by data engineers in the IT department for Big Data Analytics pipeline from ingestion until feature extraction phase. It is also being used by data scientists in Analytics department for building machine learning models. On top of that, it is also used by business analyst in Big Data Monetization business units for exploration and reporting. CDSW reduces time to market from exploring, modeling, and deploying to production.
  • Enterprise grade security.
  • Self-service analytics platform.
  • Popular programming support.
  • Lacks features offered by competition.
  • Limited license scheme options.
  • Installation in production can be challenging.
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.
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.
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September 15, 2019
Anonymous | TrustRadius Reviewer
Score 6 out of 10
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Verified User
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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.
  • The ability to use multiple languages.
  • GitHub integration.
  • Scalable.
  • Installation is difficult.
  • Upgrades are difficult.
  • Licensing options are not flexible.
The use cases are specific to my industry, and we’re implemented for experimentation and scoring of predictive models.
It is expensive and difficult to install and maintain.
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February 14, 2018
Bharadwaj (Brad) Chivukula | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User
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  • Used by the Data Science/Engineering Team as a collaboration tool.
  • Combines all the efforts of various departments under a single IDE and provides a holistic view in the retail setting.
  • Use of data to project sales numbers, marketing etc.
  • 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
  • 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
  • If you already have a Cloudera partnership and a cluster, having this is a no brainer.
  • It integrates well with your existing ecosystem and it immediately starts working on projects, accessing full datasets and share analysis and results.
  • With the inclusion of Kubernetes, CPU and memory across worker nodes can be managed effectively.
Read Bharadwaj (Brad) Chivukula's full review

Data Science Workbench Scorecard Summary

Feature Scorecard Summary

Connect to Multiple Data Sources (2)
Extend Existing Data Sources (2)
Automatic Data Format Detection (2)
MDM Integration (2)
Visualization (2)
Interactive Data Analysis (2)
Interactive Data Cleaning and Enrichment (2)
Data Transformations (2)
Data Encryption (2)
Built-in Processors (2)
Multiple Model Development Languages and Tools (2)
Automated Machine Learning (1)
Single platform for multiple model development (2)
Self-Service Model Delivery (2)
Flexible Model Publishing Options (2)
Security, Governance, and Cost Controls (2)

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.
Categories:  Hadoop-Related,  Data Science

Data Science Workbench Technical Details

Operating Systems: Unspecified
Mobile Application:No