Data Science Workbench vs Jupyter Notebook

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.

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Verified User

Professional in Information Technology

Telecommunications Company, 1001-5000 employees

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Juypter suits developers the most, who want to write small pieces of code and create larger modules. Jupyter is also suited when creating smaller integration scenarios. Jupyter enables creating neat graphical charts for analysts very quickly. It is particularly easy to create and share notebooks to who so ever. For Enterprise-grade scenarios and large scale implementations, Jupyter may not be the best fit.

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Verified User

Program Manager in Information Technology

Information Technology & Services Company, 10,001+ employees

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Feature Rating Comparison

Data Science Workbench

Jupyter Notebook

Connect to Multiple Data Sources

Extend Existing Data Sources

Automatic Data Format Detection

MDM Integration

Data Science Workbench

Jupyter Notebook

Visualization

Interactive Data Analysis

Data Science Workbench

Jupyter Notebook

Interactive Data Cleaning and Enrichment

Data Transformations

Data Encryption

Built-in Processors

Data Science Workbench

Jupyter Notebook

Multiple Model Development Languages and Tools

Automated Machine Learning

Single platform for multiple model development

Self-Service Model Delivery

Data Science Workbench

Jupyter Notebook

Flexible Model Publishing Options

Security, Governance, and Cost Controls

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

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Bharadwaj (Brad) Chivukula

Sr.Technical Manager/Delivery Manager

Nisum Technologies, Inc.Retail, 10,001+ employees

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Saves the results until next time the notebook is reopened
Provides so many libraries
Allows to code on multiple platforms
Automatically creates checkpoints
Really lightweight

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Program Manager in Information Technology

Information Technology & Services Company, 10,001+ employees

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Cons

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

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Professional in Research & Development

Utilities Company, 10,001+ employees

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Would like to see syntax errors highlighted while coding
PDF integration should get better to print notebook results

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Program Manager in Information Technology

Information Technology & Services Company, 10,001+ employees

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Usability

No score

No answers yet

No answers on this topic

Jupyter Notebook 10.0

Based on 1 answer

Jupyter is highly simplistic. It took me about 5 mins to install and create my first "hello world" without having to look for help. The UI has minimalist options and is quite intuitive for anyone to become a pro in no time. The lightweight nature makes it even more likeable.

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Program Manager in Information Technology

Information Technology & Services Company, 10,001+ employees

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Support Rating

Data Science Workbench 6.7

Based on 2 answers

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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Verified User

Professional in Information Technology

Telecommunications Company, 1001-5000 employees

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Jupyter Notebook 9.0

Based on 1 answer

I haven't had a need to contact support. However, all required help is out there in public forums.

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Verified User

Program Manager in Information Technology

Information Technology & Services Company, 10,001+ employees

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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.

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Bharadwaj (Brad) Chivukula

Sr.Technical Manager/Delivery Manager

Nisum Technologies, Inc.Retail, 10,001+ employees

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When I tried Zeppelin in 2017, it was still in initial versions, Jupyter was way ahead as of then. Zeppelin had limitations and I wasn't confident of it making progress as much as Jupyter.

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Verified User

Program Manager in Information Technology

Information Technology & Services Company, 10,001+ employees

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Return on Investment

Paid off for demonstration purposes.

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Verified User

Professional in Research & Development

Utilities Company, 10,001+ employees

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Saves Time
Easy to develop
Very easy to setup and adapt

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Verified User

Program Manager in Information Technology

Information Technology & Services Company, 10,001+ employees

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Pricing Details

General

Free Trial

—

Free/Freemium Version

—

Premium Consulting/Integration Services

—

Entry-level set up fee?

No

General

Free Trial

—

Free/Freemium Version

—

Premium Consulting/Integration Services

—

Entry-level set up fee?

No

Rating Summary

Data Science Workbench

Jupyter Notebook

Data Science Workbench

Jupyter Notebook

Data Science Workbench

Jupyter Notebook

Likelihood to Recommend