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

Running Jupyter notebooks online

Jupyter notebooks are useful for sharing insights about data. If the notebook is just on your own device, how are you supposed to share it? Sending an ipynb file to someone else is not a great experience. Ideally, you could send a link, and the recipient doesn’t have to worry about installing Jupyter or Python environments or anything like that.

A difficult way to do this is to run JupyterHub and expose it to the internet. It’s a lot of effort, but valid in some situations. If only you need to access it, running Jupyter in server mode is slightly easier. Both of these options require you to run a server such as a machine on a cloud service like AWS.

Managed, or hosted, notebooks are a much more reliable way to do this. Managed notebooks handle running Jupyter for you, and let you share notebooks with just a link. Setting them up takes minutes instead of hours.

Below is a list of fully managed Jupyter-compatible notebook tools.

A screenshot of Amazon Sagemaker

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.

A screenshot of Google Colab

Google Colab

Colab notebooks allow you to combine executable code and rich text in a single document, along with images, HTML, LaTeX and more.

A screenshot of Deepnote


Deepnote is a new kind of data notebook that’s built for collaboration — Jupyter compatible, works magically in the cloud, and sharing is as easy as sending a link.

A screenshot of Hex


The Data Workspace for Teams. Work with data in collaborative SQL and Python notebooks. Share as interactive data apps that anyone can use.

A screenshot of Databricks Notebooks

Databricks Notebooks

Collaborate across engineering, data science, and machine learning teams with support for multiple languages, built-in data visualizations, automatic versioning, and operationalization with jobs.

A screenshot of DataCamp Workspace

DataCamp Workspace

DataCamp Workspace is a cloud-based data science notebook to analyze data, collaborate with others, and share insights — no installation required.

A screenshot of CoCalc


Your best choice for teaching remote scientific courses.

A screenshot of Jetbrains Datalore

Jetbrains Datalore

A powerful online environment for Jupyter notebooks. Use smart coding assistance for Python in online Jupyter notebooks, run code on powerful CPUs and GPUs, collaborate in real-time, and easily share the results.

A screenshot of Kaggle


Explore and run machine learning code with Kaggle Notebooks, a cloud computational environment that enables reproducible and collaborative analysis.

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.

A screenshot of Noteable


Noteable is a collaborative notebook platform that enables teams to use and visualize data, together.

Data Science Notebooks

Popular notebook tools

DeepnoteGoogle ColabHexJupyterJetbrains DataloreSagemaker