What users are saying about
14 Ratings
<a href='https://www.trustradius.com/static/about-trustradius-scoring' target='_blank' rel='nofollow noopener'>trScore algorithm: Learn more.</a>Score 7.3 out of 100
Based on 14 reviews and ratings
Jupyter Notebook
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Top Rated
99 Ratings
<a href='https://www.trustradius.com/static/about-trustradius-scoring' target='_blank' rel='nofollow noopener'>trScore algorithm: Learn more.</a>Score 8.8 out of 100
Based on 99 reviews and ratings
Feature Set Ratings
- Jupyter Notebook ranks higher in 5 feature sets: Platform Connectivity, Data Exploration, Data Preparation, Platform Data Modeling, Model Deployment
Platform Connectivity
7.5
Data Science Workbench
75%
8.3
Jupyter Notebook
83%
Jupyter Notebook ranks higher in 3/4 features
Jupyter Notebook ranks higher in 3/4 features
Connect to Multiple Data Sources
7.0
70%
2 Ratings
8.5
85%
23 Ratings
Extend Existing Data Sources
8.0
80%
2 Ratings
8.6
86%
22 Ratings
Automatic Data Format Detection
7.0
70%
2 Ratings
8.5
85%
16 Ratings
MDM Integration
8.0
80%
2 Ratings
7.5
75%
17 Ratings
Data Exploration
7.6
Data Science Workbench
76%
9.2
Jupyter Notebook
92%
Jupyter Notebook ranks higher in 2/2 features
Jupyter Notebook ranks higher in 2/2 features
Visualization
7.1
71%
2 Ratings
9.3
93%
23 Ratings
Interactive Data Analysis
8.0
80%
2 Ratings
9.0
90%
23 Ratings
Data Preparation
7.8
Data Science Workbench
78%
8.5
Jupyter Notebook
85%
Jupyter Notebook ranks higher in 4/4 features
Jupyter Notebook ranks higher in 4/4 features
Interactive Data Cleaning and Enrichment
7.0
70%
2 Ratings
8.9
89%
22 Ratings
Data Transformations
8.0
80%
2 Ratings
8.7
87%
23 Ratings
Data Encryption
8.0
80%
2 Ratings
8.1
81%
16 Ratings
Built-in Processors
8.0
80%
2 Ratings
8.4
84%
15 Ratings
Platform Data Modeling
7.6
Data Science Workbench
76%
8.7
Jupyter Notebook
87%
Jupyter Notebook ranks higher in 4/4 features
Jupyter Notebook ranks higher in 4/4 features
Multiple Model Development Languages and Tools
8.0
80%
2 Ratings
8.8
88%
22 Ratings
Automated Machine Learning
7.0
70%
1 Rating
8.8
88%
20 Ratings
Single platform for multiple model development
7.1
71%
2 Ratings
8.8
88%
23 Ratings
Self-Service Model Delivery
8.1
81%
2 Ratings
8.4
84%
22 Ratings
Model Deployment
8.0
Data Science Workbench
80%
8.6
Jupyter Notebook
86%
Jupyter Notebook ranks higher in 2/2 features
Jupyter Notebook ranks higher in 2/2 features
Flexible Model Publishing Options
8.1
81%
2 Ratings
8.6
86%
21 Ratings
Security, Governance, and Cost Controls
7.8
78%
2 Ratings
8.5
85%
20 Ratings
Attribute Ratings
- Jupyter Notebook is rated higher in 2 areas: Likelihood to Recommend, Support Rating
Likelihood to Recommend
8.9
Data Science Workbench
89%
3 Ratings
9.1
Jupyter Notebook
91%
24 Ratings
Usability
Data Science Workbench
N/A
0 Ratings
10.0
Jupyter Notebook
100%
1 Rating
Support Rating
7.7
Data Science Workbench
77%
3 Ratings
9.0
Jupyter Notebook
90%
1 Rating
Likelihood to Recommend
Data Science Workbench
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.

Verified User
Professional in Information Technology
Telecommunications Company, 1001-5000 employeesJupyter Notebook
I've created a number of daisy chain notebooks for different workflows, and every time, I create my workflows with other users in mind. Jupiter Notebook makes it very easy for me to outline my thought process in as granular a way as I want without using innumerable small. inline comments.
Senior Data Scientist
VeryComputer Software, 201-500 employees
Pros
Data Science Workbench
- 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
Sr.Technical Manager/Delivery Manager
Nisum Technologies, Inc.Retail, 10,001+ employees
Jupyter Notebook
- Code presentation- with Jupyter Notebook you can deploy codes and markdowns which makes the code easy to read and understand.
- User interface- the user interface of the Jupyter Notebook is very smooth, there are a lot of easy shortcuts as well the icons to make our work easier
- Server hosting- with Jupyter Notebook server hosting is very easy which adds to the security feature
Program Associate
Wells FargoBanking, 10,001+ employees
Cons
Data Science Workbench
- Installation is difficult.
- Upgrades are difficult.
- Licensing options are not flexible.

Verified User
Professional in Research & Development
Utilities Company, 10,001+ employeesJupyter Notebook
- Need more Hotkeys for creating a beautiful notebook. Sometimes we need to download other plugins which messes [with] its default settings.
- Not as powerful as IDE, which sometimes makes [the] job difficult and allows duplicate code as it get confusing when the number of lines increases. Need a feature where [an] error comes if duplicate code is found or [if a] developer tries the same function name.

Verified User
Engineer in Research & Development
Information Technology & Services Company, 5001-10,000 employeesPricing Details
Data Science Workbench
General
Free Trial
—Free/Freemium Version
—Premium Consulting/Integration Services
—Entry-level set up fee?
No
Starting Price
—Jupyter Notebook
General
Free Trial
—Free/Freemium Version
—Premium Consulting/Integration Services
—Entry-level set up fee?
No
Starting Price
—Usability
Data Science Workbench
No score
No answers yet
No answers on this topic
Jupyter Notebook
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.

Verified User
Program Manager in Information Technology
Information Technology & Services Company, 10,001+ employeesSupport Rating
Data Science Workbench
Data Science Workbench 7.7
Based on 3 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.

Verified User
Professional in Information Technology
Telecommunications Company, 1001-5000 employeesJupyter Notebook
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.

Verified User
Program Manager in Information Technology
Information Technology & Services Company, 10,001+ employeesAlternatives Considered
Data Science Workbench
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.
Sr.Technical Manager/Delivery Manager
Nisum Technologies, Inc.Retail, 10,001+ employees
Jupyter Notebook
I have used PyCharm as well as Jupyter Notebook and for me, Jupyter wins almost every time. I really like its user-friend interface for someone who is new to python programming. The ability to run a big chunk of code part by part is a big game-changer for me. One thing I would like is a night mode just like PyCharm as it helps the night owls like myself who work a big part during the night. I also like how Anaconda command prompt helps you download a library and just a simple import statement in the notebook helps you extract every functionality.
Data Analyst
Raymour and FlaniganRetail, 1001-5000 employees
Return on Investment
Data Science Workbench
- Paid off for demonstration purposes.

Verified User
Professional in Research & Development
Utilities Company, 10,001+ employeesJupyter Notebook
- Positive impact: flexible implementation on any OS, for many common software languages
- Positive impact: straightforward duplication for adaptation of workflows for other projects
- Negative impact: sometimes encourages pigeonholing of data science work into notebooks versus extending code capability into software integration
Senior Data Scientist
VeryComputer Software, 201-500 employees