Jupyter vs. Zeppelin: What's The Difference?

Jupyter and Zeppelin are both data notebooks, also known as computational notebooks, tools that allow data scientists to document, organize, and visualize the findings of their analysis. These interactive documents provide tools you can use to collaborate and tell stories with data, with the added benefits of providing transparency and facilitating your ability to troubleshoot errors. 

Jupyter and Zeppelin are very similar programs in many ways. They are both open-source and offered by non-profit organizations; both provide flexibility with options to access your notebooks over the web or to install them locally; and both offer a variety of tools for different programming languages and integrations to give you even more flexibility over your computational notebook and the analysis you can perform. The main difference between Jupyter and Zeppelin lies in the ability to connect to Apache Spark, an open-source platform for processing and executing big data workloads. While both notebooks can connect to Apache Spark, Zeppelin is an Apache project, and so it has stronger integration features built into the program. 

Learn more about Jupyter versus Zeppelin and how you can use each for streamlined, transparent, and collaborative workflows with access to big data analysis and visualization tools. 

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What is a computational notebook?

A computational notebook is a file where data scientists and other researchers can add a combination of code, data visualizations, and written text in the same document. This interactive software lets you add different kinds of rich media and present them in one cohesive document. Using these tools, you can share your findings in a narrative way, placing executable code next to descriptive text or visualizations in the document to ensure your decisions remain documented and transparent while enabling collaborators to interact with their work. 

Some key features of computational notebooks include: 

• A method of organizing data 

• Interactive coding environment

• Tools for annotation

• Integrations and tools for data visualizations that react to real-time data

• A self-contained environment that supports reproducibility

• Access to tools for sharing and collaborating with others

What is a computational notebook used for?

Data scientists, computational researchers, machine learning engineers, chemical engineers, students, academic or scientific researchers, or any professional needing an elegant digital workflow solution can use computational notebooks to ensure nontechnical collaborators can understand the code. You can use a computational notebook to: 

• Streamline your research into one integrated workflow: Computational notebooks allow you to add code that you can run in the document next to real-time data visualizations and written text that helps explain your work. This provides an interactive environment that can streamline collaboration and your analytical workflow. 

• Explore data with powerful tools and integrations: You can use a computational notebook to analyze large data sets or run experiments to look for trends or test statistical models. 

• Foster transparency and reproducibility: You can include computational notebooks with published research so others in your field can follow your documentation and reproduce your results. 

• Simplify complex data with integrated visualization tools: You can use computational notebooks to generate real-time visualizations in the same document as the code and the text describing the visualization.

• Collaborate and share your results: Computational notebooks can connect with tools for collaboration so your colleagues or other stakeholders can access and interact with your data, as well as leave comments. The ability to combine coding and text also makes computational notebooks a great tool for teachers who want to demonstrate and explain concepts to their students. 

What is the difference between Jupyter and Zeppelin?

Both computational notebooks offer many of the same features and functionality, but the exact features and functions vary based on which one you choose. This includes how the notebooks handle visualization and integrations, the user interface, and tools like collaboration. The main difference is that Zeppelin has built-in integrations with Apache Spark and Hadoop. Jupyter can also connect with these tools, but does not have the same easy native integration as Zeppelin and Spark. Conversely, Jupyter can also connect with many other tools. 

You can find additional differences in how Zeppelin and Jupyter offer value to computational professionals. To choose between the two options, you can consider their features and how they integrate with other tools you might use. 

Jupyter vs. Zeppelin: Programming languages

Jupyter can support over 40 programming languages, including Python, Scala, Julia, and R. You can install additional language kernels to add compatibility with over 100 languages. Zeppelin’s interpreter allows you to use any backend framework in a memory-efficient way. Both of these notebooks give you access to many different tools using multiple languages (such as pandas using Python or TensorFlow using Python, JavaScript, C++, or Java). The main difference between the two is that Zeppelin offers a seamless ability to switch between one programming language and another, whereas in Jupyter, you will need to include the respective kernels for each language you’ll use. 

Jupyter vs. Zeppelin: Open-source

Project Jupyter, a non-profit organization that provides software and open standards for data science and scientific computing professionals, offers Jupyter as free, open-source software. On the other hand, Zeppelin is a free, open-source software offered by the Apache Software Foundation, a non-profit organization that provides open-source software and tools to nurture collaborative coding projects. As open-source software, you can access the source code for both of these programs for free and make customizations or integrations as needed. 

Jupyter vs. Zeppelin: Integrations

Both Zeppelin and Jupyter allow you to access a variety of tools for data analysis, visualization, and collaboration, including Apache Spark, scikit-learn, pandas, Google apps, GitHub, and much more. Developed by the open-source Apache Foundation, tools in Zeppelin have built-in integrations so you can use Apache tools in Zeppelin without requiring any additional steps. Although you will have to set up your integrations before you can start using tools like Apache Spark in Jupyter, Jupyter offers a wide range of integrations for many different kinds of functionality. 

While both notebooks can allow you to connect to Google apps, Google created Google Colaboratory based on Jupyter software. Using Colaboratory to host your computational notebooks gives you access to Google’s cloud-based computational resources and the ability to integrate Google Gemini AI with your research workflow. 

Jupyter vs. Zeppelin: Access and hosted services

You can access both Jupyter and Zeppelin through web-based applications, as well as download and host the software locally or on your network. You can also access both computational notebooks using third-party software. For example, Google Colaboratory is a web-based tool for accessing Jupyter notebooks. You can access Zeppelin through integrations with other tools for big data analysis, such as Amazon EMR. 

Jupyter vs. Zeppelin: User interface

Zeppelin has a more structured user interface where notebooks are divided into paragraphs and cells, which can be more user-friendly for some researchers. Meanwhile, Jupyter allows you to customize your document and move your sections in more dynamic ways, allowing for more flexibility. 

Jupyter vs. Zeppelin: Key considerations

Jupyter and Zeppelin are computational notebooks that have many of the same features that you can use for similar applications, although you can spot differences between the two programs that may influence you to prefer one over the other. For example, if Apache Spark or Hadoop are important components of your research, you may prefer Zeppelin for its native integrations to these tools. On the other hand, if you want to access the added functionality of Google Colab, you may prefer Jupyter notebooks. 

Learn more about Jupyter and Zeppelin on Coursera

Jupyter and Zeppelin are both computational notebooks you can use for any project that requires data processing and an elegant way to present code, research, notes, analysis, and visualizations in a streamlined workflow. You can learn more about working with Jupyter, Zeppelin, and related tools like Apache Spark and Hadoop with learning resources on Coursera. With a subscription to Coursera Plus, you can access over 10,000 learning programs, including courses where you can get hands-on experience working with a computational notebook. 

You can also explore programs like IBM’s Data Analysis and Visualization Foundations Specialization, where you can learn skills such as describing the data ecosystem, completing the tasks a data analyst performs, and exploring the knowledge and tools required for successful data analysis. 

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