The tech industry responded to the needs of data practitioners with various IDE solutions for developing code and presenting findings in a data science and machine learning context. One of the go-to solutions today is Jupyter Notebook, an open-source tool that has gained a lot of traction among data science folks and beyond.
Although Jupyter Notebook is a popular data project tool, some users may find that it lacks the capabilities they need, such as real-time collaboration, code completion, or data versioning. Using Jupyter Notebook in a project that includes several team members collaborating quickly gets painful.
This is where you need a collaborative and interactive notebook environment with the option for real-time synchronization.
What’s out there aside from Jupyter Notebook? And how do you even pick the right notebook solution for your project? Keep reading for an overview of the most popular notebooks in 2023.
How do you pick the right data science notebook for your project?
Are you a scientist or a data scientist?
Scientists are more inclined to need advanced analysis tools in their toolkit, such as numerical methods to solve differential equations. Most data scientists in the private sector are more focused on ML modeling and could do with a thinner toolkit.
While the use of Python opens the door to all scientific libraries in Python, the notebook you choose may be more/less equipped for broader scientific work.
Working alone or with a team?
There is a large set of features and integrations that allow collaboration between data scientists in their work. Sharing models, improving them together, accessing the same data sets, using code version control, data version control, and so on.
For solo work, those are less important. But for a team that needs to deliver results together, such features are a must.
How important is data visualization?
In some cases, when working with data, having a visualization of it can help optimize the models built or assess the quality of the results. Even with raw data, “feeling” the data by presenting it in the right way provides an intuitive understanding of its qualities for the task at hand.
Notebooks differ in the abilities they provide to visualize data sets, and data scientists may have different needs depending on their projects. So, choose wisely.
What’s the rest of your ecosystem?
Notebooks don’t exist in a vacuum. They need to integrate with the data, computation tools you use to execute the code you develop within the notebook, data quality and observability tools, orchestration, and so on.
Check the integrations with the rest of your environment to see if you can effortlessly work with the notebook of your choice. Day-to-day friction with flaky integrations can reduce your efficiency dramatically.
What is Jupyter Notebook and why has it gained so much traction?
Jupyter Notebook is an open-source IDE that data practitioners can start using easily. Many people use the Anaconda environment to build their data platforms, and Jupyter Notebook can easily connect to that.
Jupyter Notebook is useful for more than just providing kernels for programming languages like Python, Scala, and R. It also includes:
- Mathematical formulae, rich text, and media,
- Data gathering, cleaning, analysis, and visualization features,
- Possibility of creating and analyzing machine learning models.
Jupyter Notebook gained widespread popularity among the data science community, to the point where it has become the default tool for research. Thanks to its features, it has become the de facto choice for data scientists for sharing work, viewing and analyzing the data during the development process, prototyping, and exploratory analysis.
However, once you start scaling work on data science projects as a team, you may want to consider other options.
Let’s take a look at some other data science notebooks you might be interested in. They have the same functionality as the Jupyter Notebook, but they also allow for seamless collaboration and additional flexibility and customization.
10 alternatives to Jupyter Notebook
| Notebook | Programming languages | Version control | Cloud storage | Security | Collaboration | Other features |
|---|---|---|---|---|---|---|
| Deepnote | Python, R, Julia, SQL | – | Native | Yes | Real-time collaboration | |
| Kaggle Notebooks | Python, R | – | Native | – | – | |
| Microsoft Azure Notebooks | Python, R, F#, Julia | Git | Azure storage | via Azure Cloud | No | |
| Databricks Notebooks | Python, Scala, R | – | S3 storage | via Databricks | Yes | |
| CoCalc | Python, R, Julia | Git | Native | LaTeX, computer algebra systems | ||
| Visual Studio Code | Python, R, Java, Scala, and many others | Git | via extensions | via Microsoft | Real-time collaboration via Live Share | Code debugging |
| Google Colab | Python, R | Git | Google Drive | via Google Cloud | No real-time | |
| JetBrains Datalore | Python, R, SQL |
1. Deepnote
Deepnote is a cloud-based data science notebook platform comparable to Jupyter Notebooks but with a focus on real-time collaboration and editing. It lets users write and run code in several programming languages, as well as include text, equations, and visualizations in a single document.
Deepnote also comes with a code editor and is compatible with a wide range of libraries and frameworks. Some other useful features include:
- Querying data from BigQuery, Snowflake, and PostgreSQL using SQL.
- SQL and Python can be used in the same notebook interface without switching software.
- Python, Julia, and R are among the prominent programming languages supported.
- Deep learning frameworks such as PyTorch and TensorFlow are supported.
- It includes features for ensuring reproducibility across the team by establishing bespoke environments or importing existing DockerHub environments.
2. Kaggle Notebooks
Kaggle Notebooks is a cloud-based notebook platform for data science and machine learning enthusiasts. It provides access to hardware resources for running machine learning and deep learning models (think GPUs and TPUs).
It also includes interaction with the Kaggle API, support for data version control with Git, and the ability to easily share and collaborate on notebooks with team members.
One of the distinctions between Kaggle Notebooks and other options is that it is primarily designed for studying Python, data science, and machine learning, with an emphasis on competition.
Kaggle Notebooks is an excellent alternative for data science projects since it allows users to easily participate in these competitions and collaborate with other users and developers.
3. Microsoft Azure Notebooks
Microsoft Azure Notebooks is a cloud-based platform for data science projects and machine learning. It gives you access to hardware resources for running machine learning and deep learning models, as well as other useful features, such as integration with Microsoft Azure Storage, Git support, and the ability to easily share and collaborate on notebooks with other team members.
Microsoft Azure Notebooks supports a wide range of programming languages and libraries, including Python, R, F#, and Julia, making it a versatile platform for data practitioners, software developers, and analysts who prefer to work in their preferred programming language.
Note: You must have an Azure account to use Azure Notebooks, and the setup takes a moment.
4. Databricks Notebooks
Databricks notebooks are a popular tool for developing code and presenting findings in data science and machine learning. Databricks Notebooks support real-time multilingual coauthoring, automatic versioning, and built-in data visualizations.
Users can write code in Python, SQL, Scala, and R, as well as Personalize the setup by adding libraries of their choice. Databricks Notebooks also allow you to create regularly scheduled jobs to conduct activities, including multi-notebook workflows, automatically.
You can also browse tables and volumes, and once you’re done, export results and notebooks in HTML or ipynb formats. It’s also possible to store your notebooks, together with any associated files and dependencies, in a Git-based repository.
5. CoCalc
CoCalc (previously called SageMathCloud) is a cloud-based collaborative platform that includes many of the same features as Jupyter Notebooks as well as a number of new ones.
It supports a wide range of programming languages, including Python, R, and Julia, and gives users access to sophisticated hardware resources like GPUs.
It also supports LaTeX and computer algebra systems, making it an excellent choice for customers who require these capabilities.
6. Visual Studio Code
Visual Studio Code (VS Code) is a free and open-source integrated development environment for running and executing code easily. The tool is adaptable thanks to its many customizable extensions, code debugging, and Git integration for versioning.
Previously, VS Code was more suited to developers or engineers due to its lack of data analysis capabilities, but since 2020, the VS Code team has collaborated with the Jupyter team to create an integrated notebook within VS Code. The end result is a fantastic IDE workbook for data analysis.
The solution will feel natural to those who come from a developer background. The tool is also integrated with Git, so it has versioning capabilities – which is helpful if you’d like to track your code versions. It also includes code debugging.
7. nteract
nteract is an open-source interactive environment built for end-to-end data analysis workflows, with features such as a notebook for data exploration, application development, versioning, and more.
The interactive component of nteract means that the UI allows you to manipulate the notebook outcome and show it as an application. The environment includes a desktop app, which you can get here, and the kernel is merged seamlessly with the Anaconda environment.
Once you install nteract, you can open your notebook without having to launch the Jupyter Notebook or visit the Jupyter Lab. The nteract environment is similar to Jupyter Notebook but with more control and the possibility of extension via libraries like Papermill (notebook parameterization), Scrapbook (saving your notebook’s data and photos), and Bookstore (versioning).
8. Jupyterlite
Jupyterlite is an in-browser Jupyter Notebook developed informally by the Jupyter developer. It makes use of several Jupyter Lab and Jupyter Notebook functionalities, and users can try the sample in the Jupyter Lab style or the Retro Lab Style (Classic Jupyter Notebook).
It includes pre-installed visualization packages such as Altair, Plotly, and Matplotlib. The environment is similar to Jupyter Notebook, but it appears more basic.
9. Google Colab
Google Colaboratory (known as Colab) is a browser-based notebook created by the Google team. The environment is based on the Jupyter Notebook environment, so it will be recognizable to those of you who are already familiar with Jupyter.
The solution is great if you need access to high-performance hardware or a GPUs. Because Colab is housed on the cloud and provides a free GPU, you can analyze larger datasets that would be impossible to study on-premises, especially if you are still in the learning phase.
Simple integration with Google databases like Google Sheets, Google Drive, or Google BigQuery is another good point.
10. JetBrains Datalore
JetBrains Datalore is a cloud-based data science platform that has many of the same functions as Jupyter Notebooks as well as a few extras.
It supports a wide range of programming languages, including Python, R, and SQL, and gives users access to hardware resources such as GPUs.
One of JetBrains Datalore’s advantages is its interaction with the JetBrains ecosystem of tools, which includes IDEs like PyCharm and IntelliJ. That’s also why the tool is primarily aimed at ecosystem users.
Wrap up
Notebooks are a key tool in a data practitioner’s modern toolset that helps to write and run code, display findings, and exchange outcomes and insights. We hope that this overview gets you closer to making the best notebook choice for your project.
The lakeFS community includes users of all the notebooks presented above. They can use source control for their code using Git (if the notebook supports it), and data version control with lakeFS to get full reproducibility for their experiments.
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