Explore the Data Science Platform Stack (Jupyter, RStudio, Apache Zeppelin) stack, which is essential for anyone building data-driven applications, performing advanced analytics, and creating collaborative notebooks. This stack combines the most popular open source platforms—Jupyter, RStudio, and Apache Zeppelin—to enable efficient workflows, support multi-language development, and facilitate seamless integration with the broader data science ecosystem. Discover all the libraries and tools that make this stack a top choice for data professionals worldwide.
feature.delivery is a free, web-based platform that enables developers to monitor and consolidate software releases from multiple GitHub repositories into a single, streamlined chronological view. By centralizing release information across various tools, libraries, and services, feature.delivery simplifies the process of staying informed about the latest updates in a development stack. Stay ahead of the curve with feature.delivery, the free online tool designed to help developers effortlessly track and consolidate the latest releases from multiple GitHub repositories in one clean, chronological view. Whether you're managing a complex development stack or simply want to stay up to date with your favorite open-source projects, feature.delivery streamlines release tracking so you never miss an important update. By keeping up with the latest changes, developers can quickly adopt new features, enhance performance, and maintain a competitive edge in today's fast-moving tech landscape. Say goodbye to manual tracking and hello to smarter, faster development with feature.delivery.

https://feature.delivery

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IRkernel/
IRdisplay

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IRkernel/
IRkernel

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apache/
spark

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apache/
toree

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apache/
zeppelin

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duckdb/
duckdb

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ipython-contrib/
jupyter_contrib_nbextensions

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ipython/
ipykernel

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jupyter/
nbconvert

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jupyter/
notebook

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jupyterlab/
jupyterlab

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matplotlib/
matplotlib

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numpy/
numpy

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pandas-dev/
pandas

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plotly/
plotly.py

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rstudio/
rstudio

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sparklyr/
sparklyr

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sqlalchemy/
sqlalchemy

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tidyverse/
dplyr

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tidyverse/
ggplot2

Use this link to track latest updates across the 20 repositories in Data Science Platform Stack

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Staying up-to-date with latest features of the
Data Science Platform Stack in 2025

How does it work?

feature.delivery is a free, web-based platform that helps developers track the latest releases from multiple GitHub repositories — all in one streamlined, chronological view. By centralizing release information across tools, libraries, and frameworks, feature.delivery makes it easier than ever to stay on top of the updates throughout your development stack.

Checkout this 1 minute intro video to see it in action

The Data Science Platform Stack (Jupyter, RStudio, Apache Zeppelin) stack empowers data scientists and analysts to work seamlessly across interactive computing environments. This stack provides powerful tools for data exploration, visualization, collaborative analysis, and reproducible research. With support for multiple languages and integrations with a broad ecosystem of libraries, it accelerates the workflow from data ingestion to insightful results, making it the backbone of modern data science projects.

Here's a breakdown of the Data Science Platform Stack into different categories

Core Interactive Notebook Platforms

Fundamental platforms for interactive computing, enabling code execution, visualization, and documentation in a single place. These tools are vital for reproducible research and collaborative data analysis.

Jupyter Notebook

jupyter/notebook
Web-based interactive computational environment for creating Jupyter notebooks.
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RStudio

rstudio/rstudio
Integrated development environment for R and Python, tailored for statistical computing and graphics.
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new features in RStudio?

Apache Zeppelin

apache/zeppelin
Web-based notebook that enables data-driven, interactive data analytics and collaborative documents.
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new features in Apache Zeppelin?

Kernel and Language Support

Libraries and tools that allow interactive platforms to support multiple programming languages and execution environments, enhancing flexibility.

ipykernel

ipython/ipykernel
IPython Kernel for Jupyter, enabling Python code execution.
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IRkernel

IRkernel/IRkernel
R kernel for Jupyter, enabling the use of R in Jupyter notebooks.
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Apache Toree

apache/toree
Jupyter kernel for Scala and Spark, supporting big data analytics.
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Visualization Libraries

Essential libraries for creating rich, interactive, and publication-quality visualizations within notebooks.

matplotlib

matplotlib/matplotlib
Comprehensive library for creating static, animated, and interactive visualizations in Python.
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plotly.py

plotly/plotly.py
Interactive graphing library for Python, with support for Jupyter notebooks.
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ggplot2

tidyverse/ggplot2
A system for declaratively creating graphics, based on The Grammar of Graphics (R).
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Data Manipulation & Analysis

Powerful libraries for data wrangling, transformation, and statistical analysis, forming the core of data science workflows.

pandas

pandas-dev/pandas
Fast, powerful, flexible, and expressive data structures for Python.
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new features in pandas?

dplyr

tidyverse/dplyr
A grammar of data manipulation, providing a consistent set of verbs in R.
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new features in dplyr?

numpy

numpy/numpy
The fundamental package for scientific computing with Python.
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new features in numpy?

Big Data & Distributed Computing

Libraries and connectors for handling large-scale data processing and analytics, integrating notebook platforms with big data systems.

sparklyr

sparklyr/sparklyr
R interface for Apache Spark, enabling scalable data analysis from RStudio.
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pyspark

apache/spark
Python API for Apache Spark, used for big data analytics in Jupyter and Zeppelin.
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Data Access & Storage

Tools for accessing, querying, and storing data from various sources such as databases, cloud storage, and file formats.

sqlalchemy

sqlalchemy/sqlalchemy
Python SQL toolkit and Object Relational Mapper.
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duckdb

duckdb/duckdb
In-process SQL OLAP database management system, easily embeddable in notebooks.
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Collaboration & Sharing

Extensions and tools for sharing notebooks, managing versions, and supporting collaborative workflows.

nbconvert

jupyter/nbconvert
Jupyter tool to convert notebooks to various formats including HTML, PDF, and slides.
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jupyterlab

jupyterlab/jupyterlab
Next-generation web-based user interface for Project Jupyter.
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Notebook Extensions & Enhancements

Plugins and extensions that add new features, improve usability, and provide advanced capabilities to notebook platforms.

jupyter-contrib-nbextensions

ipython-contrib/jupyter_contrib_nbextensions
A collection of community-contributed extensions that add functionality to Jupyter notebooks.
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IRdisplay

IRkernel/IRdisplay
R package for displaying rich content in Jupyter notebooks.
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new features in IRdisplay?

The Data Science Platform Stack (Jupyter, RStudio, Apache Zeppelin) stack brings together the best tools and libraries for modern data science, analytics, and collaborative research. Explore the listed repositories to access the latest releases, features, and improvements that keep this stack at the forefront of the data science ecosystem. Click on the repository URLs to discover more and stay up to date with the evolution of this powerful stack.