TrustRadius: an HG Insights company

Data Science Workbench Reviews & Insights

Score6.7 out of 10

13 Reviews and Ratings

Get a Demo

Data Science Workbench Reviews

3 Reviews

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

Rating: 9 out of 10
Incentivized

Use Cases and Deployment Scope

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.

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.

Likelihood to Recommend

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.
VU
Verified User
Professional in Information Technology (1001-5000 employees)
Vetted Review
Cloudera Data Science Workbench
1 year of experience

Cloudera review

Rating: 6 out of 10
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

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

Cons

  • 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.
VU
Verified User
Professional in Research & Development (10,001+ employees)
Vetted Review
Cloudera Data Science Workbench
1 year of experience

Exciting tool from Cloudera

Rating: 8 out of 10
Incentivized

Use Cases and Deployment Scope

  • 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.

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

Likelihood to Recommend

  • 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.
B(
Bharadwaj (Brad) Chivukula
Sr.Technical Manager/Delivery Manager in Information Technology at Nisum Technologies, Inc. (10,001+ employees)
Vetted Review
Cloudera Data Science Workbench
1 year of experience
View profile