Jupyter Notebook vs. NVIDIA RAPIDS

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Jupyter Notebook
Score 8.9 out of 10
N/A
Jupyter Notebook is an open-source web application that allows users to create and share documents containing live code, equations, visualizations and narrative text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, data visualization, and machine learning. It supports over 40 programming languages, and notebooks can be shared with others using email, Dropbox, GitHub and the Jupyter Notebook Viewer. It is used with JupyterLab, a web-based IDE for…N/A
NVIDIA RAPIDS
Score 9.8 out of 10
N/A
NVIDIA RAPIDS is an open source software library for data science and analytics performed across GPUs.N/A
Pricing
Jupyter NotebookNVIDIA RAPIDS
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
Jupyter NotebookNVIDIA RAPIDS
Free Trial
NoNo
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoNo
More Pricing Information
Community Pulse
Jupyter NotebookNVIDIA RAPIDS
Top Pros
Top Cons
Features
Jupyter NotebookNVIDIA RAPIDS
Platform Connectivity
Comparison of Platform Connectivity features of Product A and Product B
Jupyter Notebook
8.5
22 Ratings
NVIDIA RAPIDS
9.1
2 Ratings
Connect to Multiple Data Sources8.822 Ratings9.52 Ratings
Extend Existing Data Sources8.921 Ratings9.02 Ratings
Automatic Data Format Detection8.615 Ratings9.02 Ratings
MDM Integration7.516 Ratings9.01 Ratings
Data Exploration
Comparison of Data Exploration features of Product A and Product B
Jupyter Notebook
9.4
22 Ratings
NVIDIA RAPIDS
9.5
2 Ratings
Visualization9.422 Ratings9.52 Ratings
Interactive Data Analysis9.322 Ratings9.52 Ratings
Data Preparation
Comparison of Data Preparation features of Product A and Product B
Jupyter Notebook
8.8
22 Ratings
NVIDIA RAPIDS
8.9
2 Ratings
Interactive Data Cleaning and Enrichment9.121 Ratings7.62 Ratings
Data Transformations8.822 Ratings9.52 Ratings
Data Encryption8.415 Ratings9.01 Ratings
Built-in Processors8.815 Ratings9.52 Ratings
Platform Data Modeling
Comparison of Platform Data Modeling features of Product A and Product B
Jupyter Notebook
8.8
22 Ratings
NVIDIA RAPIDS
9.2
2 Ratings
Multiple Model Development Languages and Tools9.021 Ratings9.01 Ratings
Automated Machine Learning9.019 Ratings9.52 Ratings
Single platform for multiple model development9.122 Ratings9.52 Ratings
Self-Service Model Delivery8.321 Ratings9.01 Ratings
Model Deployment
Comparison of Model Deployment features of Product A and Product B
Jupyter Notebook
8.8
20 Ratings
NVIDIA RAPIDS
9.2
2 Ratings
Flexible Model Publishing Options8.820 Ratings9.52 Ratings
Security, Governance, and Cost Controls8.719 Ratings9.01 Ratings
User Ratings
Jupyter NotebookNVIDIA RAPIDS
Likelihood to Recommend
9.1
(23 ratings)
10.0
(2 ratings)
Usability
10.0
(1 ratings)
-
(0 ratings)
Support Rating
9.0
(1 ratings)
-
(0 ratings)
User Testimonials
Jupyter NotebookNVIDIA RAPIDS
Likelihood to Recommend
Open Source
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.
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NVIDIA
NVIDIA RAPIDS drastically improves our productivity with near-interactive data science. And increases machine learning model accuracy by iterating on models faster and deploying them more frequently. It gives us the freedom to execute end-to-end data science and analytics pipelines.
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Pros
Open Source
  • Simple and elegant code writing ability. Easier to understand the code that way.
  • The ability to see the output after each step.
  • The ability to use ton of library functions in Python.
  • Easy-user friendly interface.
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NVIDIA
  • Visualization
  • Deep learning pipeline
  • State of the art libraries
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Cons
Open Source
  • 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.
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NVIDIA
  • Its not flexible and cost effective for all sizes of organizations.
  • I appreciate it has hassle-free integration.
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Usability
Open Source
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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NVIDIA
No answers on this topic
Support Rating
Open Source
I haven't had a need to contact support. However, all required help is out there in public forums.
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NVIDIA
No answers on this topic
Alternatives Considered
Open Source
With Jupyter Notebook besides doing data analysis and performing complex visualizations you can also write machine learning algorithms with a long list of libraries that it supports. You can make better predictions, observations etc. with it which can help you achieve better business decisions and save cost to the company. It stacks up better as we know Python is more widely used than R in the industry and can be learnt easily. Unlike PyCharm jupyter notebooks can be used to make documentations and exported in a variety of formats.
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NVIDIA
RAPIDS GPU accelerates machine learning to make the entire data science and analytics workflows run faster, also helps build databases and machine learning applications effectively. It also allows faster model deployment and iterations to increase machine learning model accuracy. The great value of money.
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Return on Investment
Open Source
  • 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
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NVIDIA
  • Efficient way to complete tasks
  • De-facto GPUs standard
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