Overview

Apache Zeppelin

Stacks191

Followers306

Votes32

GitHub Stars6.6K

Forks2.8K

Jupyter

Stacks2.7K

Followers1.4K

Votes57

GitHub Stars12.7K

Forks5.5K

Apache Zeppelin vs Jupyter: What are the differences?

Apache Zeppelin and Jupyter are both popular web-based notebooks used for data analysis and visualization. While they share many similarities, there are several key differences between the two.

Data Visualization: Apache Zeppelin provides built-in support for a wide range of visualizations, making it easier for users to create and customize charts, graphs, and dashboards. In contrast, Jupyter relies on external libraries such as Matplotlib and Plotly for data visualization.
Multi-Language Support: Zeppelin supports multiple programming languages such as Scala, Python, R, and SQL in a single notebook, allowing users to seamlessly switch between different languages. Jupyter, on the other hand, requires the use of separate kernels for different languages.
Collaboration and Sharing: Zeppelin offers built-in collaboration features, allowing multiple users to work on the same notebook simultaneously. It also provides easy sharing options for notebooks. Jupyter, while it supports collaboration through external tools like JupyterHub, does not have native collaboration features.
Integration: Zeppelin integrates well with Apache Spark and Hadoop, making it a preferred choice for big data analysis. Jupyter, on the other hand, offers more flexibility and can be easily integrated with a wide range of libraries and frameworks.
User Interface: Zeppelin has a more structured and organized user interface, with notebooks divided into paragraphs and cells. Jupyter has a more flexible and notebook-centric interface, allowing users to rearrange and customize cells as needed.
Community and Ecosystem: Jupyter has a larger and more active community, with a wide range of extensions, plugins, and libraries available for users. Zeppelin, while it has a growing community, may have a more limited ecosystem in comparison.

In summary, Apache Zeppelin and Jupyter are both powerful tools for data analysis, but they differ in terms of data visualization, multi-language support, collaboration features, integration capabilities, user interface, and community ecosystem. The choice between the two would depend on the specific requirements and preferences of the user.

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Detailed Comparison

Apache Zeppelin	Jupyter
A web-based notebook that enables interactive data analytics. You can make beautiful data-driven, interactive and collaborative documents with SQL, Scala and more.	The Jupyter Notebook is a web-based interactive computing platform. The notebook combines live code, equations, narrative text, visualizations, interactive dashboards and other media.
Statistics
GitHub Stars 6.6K	GitHub Stars 12.7K
GitHub Forks 2.8K	GitHub Forks 5.5K
Stacks 191	Stacks 2.7K
Followers 306	Followers 1.4K
Votes 32	Votes 57
Pros & Cons
Pros 7 In-line code execution using paragraphs 5 Cluster integration 4 In-line graphing 4 Multi-User Capability 4 Zeppelin context to exchange data between languages	Pros 19 In-line code execution using blocks 11 In-line graphing support 8 Can be themed 7 Multiple kernel support 3 Best web-browser IDE for Python
Integrations
Cassandra Apache Spark R Language PostgreSQL Elasticsearch HBase Hadoop Apache Flink Python	GitHub scikit-learn Scala Python Dropbox Apache Spark Pandas TensorFlow R Language ggplot2

What are some alternatives to Apache Zeppelin, Jupyter?

Deepnote

Deepnote is building the best data science notebook for teams. In the notebook, users can connect their data, explore and analyze it with real-time collaboration and versioning, and easily share and present the polished assets to end users.

Google Colaboratory

It is a hosted Jupyter Notebook service that requires no setup to use and provides free access to computing resources, including GPUs and TPUs. Colab is especially well suited to machine learning, data science, and education.

SageMath

It is a free open-source mathematics software system licensed under the GPL. It builds on top of many existing open-source packages: NumPy, SciPy, matplotlib, Sympy, Maxima, GAP, FLINT, R and many more.

Polynote

It is a different kind of notebook. It supports mixing multiple languages in one notebook, and sharing data between them seamlessly. It encourages reproducible notebooks with its immutable data model.

Franchise

Chart with a single click. Compare queries side by side. Download your work and share it with anyone. If your data is in a CSV, JSON, or XLSX file, loading it is as simple as dropping the file into Franchise.

Vayu

It is an end-to-end tool for data science, without writing any code. Import, prepare, analyze, visualize and share in just a few clicks. Build interactive reports, automate workflows and share templates.

Starboard

It is fully in-browser literate notebooks like Jupyter Notebook. It's probably the quickest way to visualize some data with interactivity, do some prototyping, or build a rudimentary dashboard.

Marimo

It is a reactive notebook for Python. It allows you to rapidly experiment with data and models, code with confidence in your notebook’s correctness, and productionize notebooks as pipelines or interactive web apps.

mljar Mercury

It is the easiest way to turn your Python Notebooks into interactive web applications and publish to the cloud. It is dual-licensed. The main features are available in the open-source version. It is perfect for quick demos, educational purposes, sharing notebooks with friends.

Mineo

It is the platform to explore your data, develop and deploy your Python supercharged Notebooks and track the quality of your data using Machine learning.