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

Amazon Sagemaker vs Nextjournal

Comparing two data science notebooks.

Amazon Sagemaker

Nextjournal

A screenshot of Amazon Sagemaker

Amazon SageMaker helps data scientists and developers to prepare, build, train, and deploy high-quality machine learning (ML) models quickly by bringing together a broad set of capabilities purpose-built for ML.

WebsiteAlternatives
A screenshot of Nextjournal

Runs anything you can put into a Docker container. Improve your workflow with polyglot notebooks, automatic versioning and real-time collaboration. Save time and money with on-demand provisioning, including GPU support.

WebsiteAlternatives

Setup

Is it managed?

Fully managed (setup in hours)

Is it managed?

Fully managed (setup in minutes)

Can you self-host?

No, you must use a managed offering

Can you self-host?

No, you must use a managed offering

Features

Is it Jupyter compatible?

Jupyter-compatible

Is it Jupyter compatible?

Jupyter-compatible

Programming languages

Jupyter languages (e.g. Python, R)

Programming languages

Jupyter languages (e.g. Python, R)

What kind of data sources can you connect to?

Connect with Jupyter libraries (e.g. SQLAlchemy, psycopg2)
AWS

What kind of data sources can you connect to?

Connect with Jupyter libraries (e.g. SQLAlchemy, psycopg2)
Connect to data warehouses (AWS S3, Google Cloud Storage, etc.)
Connect to databases (SQLite, PostgreSQL, etc.)

What kind of data visualization can you do?

Jupyter data visualization (e.g. Matplotlib, Altair, Plotly)

What kind of data visualization can you do?

Jupyter data visualization (e.g. Matplotlib, Altair, Plotly)

Reactivity

No reactivity, you decide the execution order

Reactivity

No reactivity, you decide the execution order

Notebook scheduling

Notebook scheduling with additional tools

Notebook scheduling

Notebook scheduling is built in

Management

Reproducibility

There is no support for reproducibility

Reproducibility

Environments are reproducible by default
Run notebooks in containers

Version history

File-based (use Git)

Version history

Version history is built in

Collaborative editing

No support for collaborative editors

Collaborative editing

Multiple editors at the same time

Comments

File-based (use GitHub)

Comments

Comment on items within a notebook

Notebook organization

View notebooks in a list

Notebook organization

View notebooks in a list

Licensing

License

Proprietary

License

Proprietary

Price

Free tier
Pay for compute

Price

Free tier
Pay-per-user
Pay for compute

Alternatives to Amazon Sagemaker and Nextjournal

Compare Amazon Sagemaker and Nextjournal with other data science notebook tools.

ToolSetupJupyter compatibilityProgramming languagesData visualizationCollaborative editingPricingLicense
Amazon Sagemaker		Fully managedJupyter-compatibleJupyterVisualize with codeLimited collaborationFree and paid optionsProprietary
Nextjournal		Fully managedJupyter-compatibleJupyterVisualize with codeRealtime collaborationFree and paid optionsProprietary
Jupyter
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Data Science Notebooks

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