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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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Learn from top reviewers

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

Rating: 9 out of 10
November 18, 2020
IncentivizedVetted ReviewVerified User
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 phas...
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Verified user
Professional
Cloudera Data Science Workbench1 year of experience

Cloudera review

Rating: 6 out of 10
September 15, 2019
IncentivizedVetted ReviewVerified User
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 Ma...
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Verified user
Professional
Cloudera Data Science Workbench1 year of experience

Exciting tool from Cloudera

Rating: 8 out of 10
February 14, 2018
IncentivizedVetted ReviewVerified User
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...
Continue reading
Bharadwaj (Brad) Chivukula
Manager - Information Technology
Sr.Technical Manager/Delivery Manager
Nisum Technologies, Inc.
Cloudera Data Science Workbench1 year of experience
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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 From Top Reviewers

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(1-2 of 2)

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

Rating: 9 out of 10
Incentivized
November 18, 2020
Verified User
Professional in Information Technology
Telecommunications Company, 1001-5000 employees
Vetted Review
Verified User
Cloudera Data Science Workbench
1 year of experience
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.
Data Science Workbench Feature Ratings
Platform Connectivity (4)
75%
7.5
Connect to Multiple Data Sources
70%
7.0
Extend Existing Data Sources
80%
8.0
Automatic Data Format Detection
70%
7.0
MDM Integration
80%
8.0
Data Exploration (2)
75%
7.5
Visualization
70%
7.0
Interactive Data Analysis
80%
8.0
Data Preparation (4)
77.5%
7.8
Interactive Data Cleaning and Enrichment
70%
7.0
Data Transformations
80%
8.0
Data Encryption
80%
8.0
Built-in Processors
80%
8.0
Platform Data Modeling (4)
75%
7.5
Multiple Model Development Languages and Tools
80%
8.0
Automated Machine Learning
70%
7.0
Single platform for multiple model development
70%
7.0
Self-Service Model Delivery
80%
8.0
Model Deployment (2)
80%
8.0
Flexible Model Publishing Options
80%
8.0
Security, Governance, and Cost Controls
80%
8.0
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)
Since our organization had already implemented Cloudera Data Platform as our Big Data Warehouse platform, implementing CDSW as the go-to Analytic and Data Science Platform is the most logical and cost-effective decision to make. It integrates seamlessly with our CDH clusters and it also provides enterprise-grade security for on-premise implementation.
Support Rating
8
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.
Other Software Used

Exciting tool from Cloudera

Rating: 8 out of 10
Incentivized
February 14, 2018
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.
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
Both the tools have similar features and have made it pretty easy to install/deploy/use. Depending on your existing platform (Cloudera vs. Azure) you need to pick the Workbench. Another observation is that Cloudera has better support where you can get feedback on your questions pretty fast (unlike MS). As its a new product, I expect MS to be more efficient in handling customers questions.
Other Software Used
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