Exploring Online Jupyter Notebooks

Dive deep into the world of online Jupyter notebooks, exploring their functionality, highlighting their advantages, and illustrating how they are revolutionizing the way we approach data science.

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In the rapidly evolving landscape of data science and programming, tools that foster simplicity, efficiency, and collaboration, are highly coveted. One such tool that has proven to be indispensable to data scientists, researchers, and educators alike is Jupyter Notebook. Born from the IPython project, Jupyter Notebook provides an interactive computational environment where you can combine code execution, rich text, mathematics, plots, and rich media into a single document.

While Jupyter notebooks started as a local, desktop-based application, recent years have witnessed a significant shift towards their online counterparts.

The move to online Jupyter notebooks has not just been a technological shift but a paradigm shift. It has been about embracing the cloud, enhancing collaboration, and making data science more accessible and scalable.

This article dives into the world of online Jupyter notebooks, exploring their functionality, highlighting their advantages, and illustrating how they are revolutionizing the way we approach data science.

What are Online Jupyter Notebooks?

Online Jupyter notebooks, as the name suggests, are cloud-based versions of the classic Jupyter notebook. They bring the interactive computing environment of Jupyter notebooks to your web browser, eliminating the need for any local setup or dedicated hardware resources. You can write code, run it, see the output, visualize data, and write documentation, all in one place, directly from your browser.

Online Jupyter notebooks work by connecting your browser to a kernel (the computational engine) running on a server in the cloud. When you run a code cell in the notebook, the code is sent to the kernel, executed there, and the results are returned to your notebook. This means you can leverage the computational power of high-performance servers, even from a low-spec device.

Online Jupyter notebooks support multiple programming languages, including Python, R, and Julia, among others. They also come with many popular libraries pre-installed, which makes it easier to get started with your data analysis or machine learning project.

So, why are people using online Jupyter notebooks? The reasons are manifold:

  • No setup required: You don’t need to install Python, Jupyter, or any libraries on your machine. You can start coding right away, which is particularly useful for beginners and educators.

  • Accessibility: As long as you have an internet connection, you can access your notebooks from anywhere, on any device, be it your home computer, your office laptop, or your tablet.

  • Collaboration: Online Jupyter notebooks allow multiple users to work on the same notebook at the same time, similar to Google Docs. This makes it a great tool for team projects, pair programming, or education settings.

  • Scalability: With online notebooks, you can easily scale up your computational resources as needed. Some platforms even offer access to GPUs or TPUs for intensive computations.

  • Integration with cloud storage: You can easily link your notebooks to your cloud storage (like Google Drive or GitHub), which simplifies the process of saving, sharing, and versioning your work.

In the next sections, we will explore some of the most popular platforms for online Jupyter notebooks, walk you through the process of setting up and using an online notebook, and delve into more advanced features and functionalities.

Key Online Jupyter Notebooks Platforms

The world of online Jupyter notebooks is rich and diverse, with multiple platforms vying to offer the best features and services to users. Here, we’ll explore four key platforms: Saturn Cloud, Google Colab, Deepnote, and Microsoft Azure Notebooks.

Saturn Cloud

Saturn Cloud is a platform tailored for data scientists, offering scalable resources for Jupyter notebooks, Dask, and machine learning. It supports both CPU and GPU instances and integrates seamlessly with Dask for distributed computing, making it an excellent choice for large scale data processing tasks. Saturn Cloud also enables real-time collaboration, making it easy for teams to work together on a single notebook. Its integration with GitHub and S3 ensures smooth version control and data access. It operates on a freemium model, with the free tier offering limited resources and the premium tiers offering more powerful computational resources and priority support.

Google Colab

Google Colab, or Colaboratory, is a free Jupyter notebook environment that runs entirely in the cloud. It offers free access to computing resources, including GPUs and TPUs. Google Colab integrates well with Google Drive and GitHub, allowing users to import/export notebooks easily. It’s an excellent choice for machine learning and data analysis projects, especially for individual users or small teams. However, it lacks some features for real-time collaboration and its free version has usage limits, which might be restrictive for larger or more intensive projects.


Deepnote is a relatively new player in the online Jupyter notebook scene but has quickly made a name for itself with its sleek interface and innovative features. Deepnote notebooks offer real-time collaboration similar to Google Docs, including comments and tasks, making it a powerful tool for teamwork. The platform also provides robust integration with various data sources and tools, such as GitHub, Docker, and various SQL databases. Deepnote operates on a freemium model, with the free tier offering limited resources and the premium tiers providing more resources and advanced features.

Microsoft Azure Notebooks

Azure Notebooks is Microsoft’s contribution to the online Jupyter notebook environment. It offers seamless integration with the Azure ecosystem, which could be a significant advantage for users already invested in Azure services. Azure Notebooks support multiple programming languages and provide free access to a limited amount of computational resources. However, as of my knowledge cutoff in September 2021, Microsoft had announced the retirement of Azure Notebooks, with existing projects being migrated to Visual Studio Codespaces.

Amazon SageMaker

SageMaker offers pre-configured Jupyter notebook instances with optimized environments for machine learning tasks. Users can choose from a range of instance types, including CPU and GPU options, depending on their computational requirements. This flexibility makes SageMaker suitable for a wide range of use cases, from small-scale experimentation to large-scale training and deployment.

In addition to standard Jupyter notebook functionality, SageMaker provides built-in support for distributed training. It allows users to train machine learning models across multiple instances, speeding up training time for computationally intensive tasks. SageMaker also offers automatic model tuning and hyperparameter optimization to help data scientists find the best model configuration


When comparing these platforms, several factors come into play. Saturn Cloud shines with its distributed computing capabilities and seamless integration with Dask. Saturn Cloud and Google Colab are popular for their generous free computational resources. Deepnote stands out for its real-time collaboration features, and Azure Notebooks for its integration with the Azure ecosystem.

The choice of platform depends on your specific needs, including the scale of your project, the need for collaboration, the programming languages and libraries you use, and your budget. All four platforms offer a way to run Jupyter notebooks in the cloud, but each has its unique strengths and features that make it suitable for different use-cases.

Collaborative Features of Online Jupyter Notebooks

One of the most compelling features of some online Jupyter notebooks, such as Deepnote and Google Colab, is their facilitation of real-time collaboration. This allows multiple users to simultaneously work on the same notebook, akin to a Google Docs experience. Team members can view each other’s modifications live, making brainstorming, problem-solving, and pair programming more interactive and efficient. This feature is particularly beneficial in an educational setting where a teacher can view and guide students' work in real-time, or in corporate environments where teams can collaboratively develop and refine models.

Sharing and publishing notebooks is another significant advantage. With just a few clicks, you can share a link to your notebook with colleagues or the public. Some platforms even allow for notebook embedding into websites or blogs, making it easier to showcase your work or findings. This simple sharing process fosters open science and enables a more seamless exchange of ideas within the data science community.

Version control integration is another key feature of many online Jupyter notebook platforms. The ability to track changes over time and revert to earlier versions of the notebook if necessary can be a lifesaver. This feature is often implemented through integration with GitHub, allowing users to commit changes to a repository directly from the notebook interface.

Advanced Functionalities in Online Jupyter Notebooks

Online Jupyter notebooks also offer advanced functionalities to enhance computational capabilities. Many platforms provide access to GPUs and TPUs, allowing users to run more computationally intensive tasks, such as training large deep learning models. Users can often choose their level of computational resources, scaling up or down based on the complexity of the task at hand. This flexibility makes online notebooks a viable option for a wide range of projects, from simple data analysis tasks to sophisticated machine learning applications.

Moreover, online notebooks integrate with cloud storage solutions, enabling users to import data directly from services like Google Drive, Dropbox, or S3. They also often offer an auto-save feature, ensuring that work is not lost in case of a browser crash or internet disconnection. Some platforms also maintain a revision history, allowing users to see previous versions of their notebook, which can be crucial for tracking changes and maintaining a record of the project’s evolution. These features, combined with the ones discussed earlier, make online Jupyter notebooks an incredibly powerful tool for modern data science workflows.

Step-by-Step Guide to Using an Online Jupyter Notebook on Saturn Cloud

Setting up and using an online Jupyter notebook on Saturn Cloud is a relatively straightforward process. Here’s a step-by-step guide:

Setting Up Your Environment

Create an Account: Visit the Saturn Cloud website and sign up for a new account. You can choose between the free tier and various paid options based on your needs. There is also an option for Saturn Cloud Enterprise, which installs Saturn Cloud into your AWS account where you can securely connect and store your data and code.

Log in to Your Account: Once you’ve created your free account, log in. You’ll be directed to the Saturn Cloud dashboard.

Create a New Project: On the Resources », New Python Server or New R Server » Give your project a name and a description. You can also choose the size of the machine (CPU/GPU and memory) that you want to use, packages you want to import, connecting to Git, and more.

Create a New Project

Creating a Notebook and Running Code

Open Your Project: Once you’ve created your project, click on it in the dashboard to open it. Then, click on the “Start Jupyter” button to start your Jupyter server.

Create a New Notebook: In the Jupyter interface, click on “New” and select “Python 3” to create a new notebook. You’ll see a familiar Jupyter notebook interface with an empty cell at the top.

Write and Run Code: You can write your Python code in the cell and run it by clicking on the “Run” button or using the Shift + Enter keyboard shortcut. The output will be displayed directly under the cell.

Importing/Exporting Data

Importing Data: You can import data from various sources. If you’re using a file from your local machine, click on the “Upload” button in the Jupyter interface and select your file. If you’re importing data from a URL or a cloud storage service like S3, you can use appropriate Python libraries (like pandas) to load your data.

Exporting Data: You can export your data or your entire notebook for use outside of Saturn Cloud. To export a notebook, click on “File” and then “Download as”. You can choose to download the notebook as a .ipynb file, an HTML file, or various other formats.

Remember to stop your Jupyter server when you’re done working to save your computational resources. You can do this from the Saturn Cloud dashboard. With this guide, you should be able to get started with running Jupyter notebooks on Saturn Cloud.


The transition to online Jupyter notebooks is a testament to the ever-evolving nature of data science and the tools that support it. As we’ve explored, these cloud-based notebooks offer a plethora of advantages over their local counterparts, including the ease of setup, accessibility, and scalability, along with advanced features like real-time collaboration and version control.

From robust distributed computing capabilities to the free computing resources, each platform we covered brings unique strengths to the table, and the choice ultimately depends on your specific needs and preferences.

We’ve also walked through how to set up and run code on an online Jupyter notebook, using Saturn Cloud as an example. The process, as we’ve seen, is quite straightforward, and with the plethora of online resources and supportive communities surrounding these platforms, even beginners can quickly get up to speed.

The move to online Jupyter notebooks represents not just a shift in how we perform data science and coding tasks, but also a shift towards a more collaborative, open, and accessible way of learning and working. Whether you’re a seasoned data scientist, a student just starting out, or an educator, online Jupyter notebooks offer an effective and efficient tool to drive your data science endeavors. As these platforms continue to evolve and improve, we can expect to see even more features and innovations that will further streamline and enhance our data science workflows.

Additional Resources

To further your understanding and skills in working with online Jupyter notebooks, here are some additional resources:

Jupyter Project Official Documentation: The official documentation of the Jupyter project is a great place to start. It provides a comprehensive guide on Jupyter notebooks and their features.

Saturn Cloud Documentation: If you’re interested in delving deeper into Saturn Cloud, their official documentation is an invaluable resource.

Project Jupyter’s GitHub: The GitHub repository of Project Jupyter hosts the source code for all Jupyter projects and is a good place to explore if you’re interested in the technical aspects of Jupyter notebooks.

Jupyter Notebook Extensions: To enhance your Jupyter notebook experience, you can use extensions. This GitHub repository provides a collection of extensions that add functionalities to Jupyter notebooks.

A Simple Guide to Jupyter Notebook Extensions: Understand the different types of Jupyter Notebook extensions and how to use them.

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