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Data Science Notebooks

Data science gets done in notebooks. This website exists to compare the features in different data science notebook tools.

Robert Lacok
About the author

My name is Robert Lacok, and I’m a data notebook enthusiast. Because I keep on top of the latest developments in the space, I wanted to share it with the world.

I’m also a product manager at Deepnote. I try to be unbiased — if you believe any tools are missing or misrepresented, please email me or open a pull request on GitHub.

Need help?

If you need help picking a data notebook for your next project, feel free to reach out to me at my personal email address. I’d be happy to chat about the pros and cons of each solution.

Mode Notebooks

A screenshot of Mode Notebooks

Native R & Python Notebooks. From SQL, explore your analysis using R or Python Notebooks.

WebsiteAlternatives

Setup

Is it managed?

Fully managed (setup in minutes)

Can you self-host?

No, you must use a managed offering

Features

Is it Jupyter compatible?

Not Jupyter-compatible

Programming languages

SQL
R
Python

What kind of data sources can you connect to?

Connect to databases (MariaDB, PostgreSQL, etc.)
Connect to data warehouses (Amazon Redshift, Google BigQuery, etc.)

What kind of data visualization can you do?

UI for building charts

Reactivity

No reactivity, you decide the execution order

Notebook scheduling

Notebook scheduling is built in

Management

Reproducability

Environments are reproducible by default

Version history

No version history

Collaborative editing

Multiple editors, one at a time (asynchronous)

Comments

No support for comments

Notebook organization

View notebooks in a list

Licensing

License

Proprietary

Price

Unknown

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Data Science Notebooks

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