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Cloudera reviewCloudera 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.,6,Paid off for demonstration purposes.,5Exciting tool from ClouderaUsed 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,8,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,Microsoft Azure Machine Learning Workbench,Hadoop, HBase, Apache Solr
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Cloudera Data Science Workbench
13 Ratings
Score 7.5 out of 101
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Data Science Workbench Reviews

Data Science Workbench
13 Ratings
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Score 7.5 out of 101

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September 15, 2019

Cloudera review

Score 6 out of 10
Vetted Review
Verified User
Review Source
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.
Read this authenticated review
Bharadwaj (Brad) Chivukula profile photo
Score 8 out of 10
Vetted Review
Verified User
Review Source
  • 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 (1)
6
Extend Existing Data Sources (1)
7
Automatic Data Format Detection (1)
7
MDM Integration (1)
8
Visualization (1)
9
Interactive Data Analysis (1)
9
Interactive Data Cleaning and Enrichment (1)
8
Data Transformations (1)
8
Data Encryption (1)
8
Built-in Processors (1)
7
Multiple Model Development Languages and Tools (1)
9
Single platform for multiple model development (1)
10
Self-Service Model Delivery (1)
10
Flexible Model Publishing Options (1)
10
Security, Governance, and Cost Controls (1)
4

About 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