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.

View all notebooks

Observable vs Zeppelin

Comparing two data science notebooks.

Observable

Zeppelin

A screenshot of Observable

Make sense of the world with data, together. Explore, visualize, and analyze data. Collaborate with the community. Learn and be inspired. Share insights with the world.

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A screenshot of Zeppelin

Web-based notebook that enables data-driven, interactive data analytics and collaborative documents with SQL, Scala and more.

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Setup

Is it managed?

Fully managed (setup in minutes)

Is it managed?

No, you must host it yourself

Can you self-host?

No, you must use a managed offering

Can you self-host?

You can self-host (setup in hours)

Features

Is it Jupyter compatible?

Not Jupyter-compatible

Is it Jupyter compatible?

Not Jupyter-compatible

Programming languages

JS

Programming languages

Python
SQL
Spark

What kind of data sources can you connect to?

Connect with JS libraries (e.g. REST APIs)
Connect to databases (MySQL, Postgres)
Connect to data warehouses (BigQuery, Snowflake)
Provided file storage

What kind of data sources can you connect to?

What kind of data visualization can you do?

JS data visualization (e.g. D3, Vega)

What kind of data visualization can you do?

Unknown

Reactivity

Full, realtime reactivity

Reactivity

No reactivity, you decide the execution order

Notebook scheduling

No notebook scheduling

Notebook scheduling

Notebook scheduling is built in

Management

Reproducibility

Environments are reproducible by default
Execution is reproducible by default

Reproducibility

With effort, you can make reproducible environments

Version history

Version history is built in

Version history

File-based (use Git)

Collaborative editing

Multiple editors at the same time

Collaborative editing

File-based (use Git)
Multiple editors, one at a time (asynchronous)

Comments

Comment on items within a notebook

Comments

File-based (use GitHub)

Notebook organization

View notebooks in a list

Notebook organization

File-based

Licensing

License

Proprietary

License

Open-source (Apache 2.0)

Price

Free tier
Free trial (1 month)
Pay-per-user

Price

Free

Alternatives to Observable and Zeppelin

Compare Observable and Zeppelin with other data science notebook tools.

ToolSetupJupyter compatibilityProgramming languagesData visualizationCollaborative editingPricingLicense
Observable		Fully managedNoneJSVisualize with codeRealtime collaborationFree and paid optionsProprietary
Zeppelin		Self-hostedNonePython, SQL, SparkUnknownFile-based or asynchronous collaborationFreeOpen source
Jupyter
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Observable, Zeppelin
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Data Science Notebooks

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Notebook tools by feature

Jupyter notebooks onlineScheduling Jupyter notebooksRealtime collaboration in Jupyter notebooksVersion control in Jupyter notebooksComments in Jupyter notebooksOpen source notebooks