Jupyter Notebook vs. Redash

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
Redash
Score 7.5 out of 10
N/A
Redash is a data visualization tool designed to allow users to connect and query any data sources, build dashboards to visualize data and share them with a company. Databricks acquired Redash in June 2020.N/A
Pricing
Jupyter NotebookRedash
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
Jupyter NotebookRedash
Free Trial
NoNo
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details
More Pricing Information
Community Pulse
Jupyter NotebookRedash
Features
Jupyter NotebookRedash
Platform Connectivity
Comparison of Platform Connectivity features of Product A and Product B
Jupyter Notebook
9.0
22 Ratings
7% above category average
Redash
-
Ratings
Connect to Multiple Data Sources10.022 Ratings00 Ratings
Extend Existing Data Sources10.021 Ratings00 Ratings
Automatic Data Format Detection8.514 Ratings00 Ratings
MDM Integration7.415 Ratings00 Ratings
Data Exploration
Comparison of Data Exploration features of Product A and Product B
Jupyter Notebook
7.0
22 Ratings
18% below category average
Redash
-
Ratings
Visualization6.022 Ratings00 Ratings
Interactive Data Analysis8.022 Ratings00 Ratings
Data Preparation
Comparison of Data Preparation features of Product A and Product B
Jupyter Notebook
9.5
22 Ratings
15% above category average
Redash
-
Ratings
Interactive Data Cleaning and Enrichment10.021 Ratings00 Ratings
Data Transformations10.022 Ratings00 Ratings
Data Encryption8.514 Ratings00 Ratings
Built-in Processors9.314 Ratings00 Ratings
Platform Data Modeling
Comparison of Platform Data Modeling features of Product A and Product B
Jupyter Notebook
9.3
22 Ratings
10% above category average
Redash
-
Ratings
Multiple Model Development Languages and Tools10.021 Ratings00 Ratings
Automated Machine Learning9.218 Ratings00 Ratings
Single platform for multiple model development10.022 Ratings00 Ratings
Self-Service Model Delivery8.020 Ratings00 Ratings
Model Deployment
Comparison of Model Deployment features of Product A and Product B
Jupyter Notebook
10.0
20 Ratings
15% above category average
Redash
-
Ratings
Flexible Model Publishing Options10.020 Ratings00 Ratings
Security, Governance, and Cost Controls10.019 Ratings00 Ratings
BI Standard Reporting
Comparison of BI Standard Reporting features of Product A and Product B
Jupyter Notebook
-
Ratings
Redash
6.9
4 Ratings
17% below category average
Pixel Perfect reports00 Ratings7.04 Ratings
Customizable dashboards00 Ratings7.84 Ratings
Report Formatting Templates00 Ratings5.84 Ratings
Ad-hoc Reporting
Comparison of Ad-hoc Reporting features of Product A and Product B
Jupyter Notebook
-
Ratings
Redash
6.1
4 Ratings
27% below category average
Drill-down analysis00 Ratings5.84 Ratings
Formatting capabilities00 Ratings7.84 Ratings
Integration with R or other statistical packages00 Ratings2.73 Ratings
Report sharing and collaboration00 Ratings8.04 Ratings
Report Output and Scheduling
Comparison of Report Output and Scheduling features of Product A and Product B
Jupyter Notebook
-
Ratings
Redash
5.4
4 Ratings
43% below category average
Publish to Web00 Ratings8.02 Ratings
Publish to PDF00 Ratings7.04 Ratings
Report Versioning00 Ratings5.53 Ratings
Report Delivery Scheduling00 Ratings2.63 Ratings
Delivery to Remote Servers00 Ratings3.93 Ratings
Data Discovery and Visualization
Comparison of Data Discovery and Visualization features of Product A and Product B
Jupyter Notebook
-
Ratings
Redash
6.4
4 Ratings
22% below category average
Pre-built visualization formats (heatmaps, scatter plots etc.)00 Ratings7.04 Ratings
Location Analytics / Geographic Visualization00 Ratings7.52 Ratings
Predictive Analytics00 Ratings4.23 Ratings
Pattern Recognition and Data Mining00 Ratings7.01 Ratings
Best Alternatives
Jupyter NotebookRedash
Small Businesses
IBM Watson Studio
IBM Watson Studio
Score 9.9 out of 10
Supermetrics
Supermetrics
Score 9.7 out of 10
Medium-sized Companies
Posit
Posit
Score 9.9 out of 10
Supermetrics
Supermetrics
Score 9.7 out of 10
Enterprises
Posit
Posit
Score 9.9 out of 10
Dataiku
Dataiku
Score 8.4 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Jupyter NotebookRedash
Likelihood to Recommend
10.0
(23 ratings)
8.7
(4 ratings)
Usability
10.0
(2 ratings)
-
(0 ratings)
Support Rating
9.0
(1 ratings)
-
(0 ratings)
User Testimonials
Jupyter NotebookRedash
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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Databricks
Redash is well suited to situations where metrics are tracked on daily, weekly and monthly basis. Alerts can be set to emails which helps stakeholders to monitor performance on a frequent basis. It is less appropriate for cases where only dashboards are needed. Redash comes into picture where individuals can query and check data at the same time.
Read full review
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.
Read full review
Databricks
  • Great Query Editor with Autocomplete feature
  • Very easy to setup and quickly connect to variety of data sources
  • Quick Dashboards with Simple UI which can be easily shareable
Read full review
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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Databricks
  • You need to have a good command over SQL to use Redash but if there could be some way where people can just pull data and do slice dice.
  • It would be nice to have an excel kind of filters when all data is fetched.
  • Some things like easy to customise the column names.
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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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Databricks
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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Databricks
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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Databricks
I was not a part of the decision-making team who decided to go with Redash.
Read full review
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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Databricks
  • Cost effective
  • One tool for multiple purpose
  • Easy access provision
Read full review
ScreenShots