Jupyter vs Observable: a side-by-side comparison for 2024
Comparing two data science notebooks.
Jupyter
Project Jupyter exists to develop open-source software, open-standards, and services for interactive computing across dozens of programming languages. There's a number of vendors offering Jupyter notebooks as a managed service.
Observable
Make sense of the world with data, together. Explore, visualize, and analyze data. Collaborate with the community. Learn and be inspired. Share insights with the world.
Jupyter
Observable
Setup
Is it managed?
Is it managed?
No, you must host it yourself
Fully managed (setup in minutes)
Can you self-host?
Can you self-host?
You can self-host (setup in hours)
No, you must use a managed offering
Features
Is it Jupyter compatible?
Is it Jupyter compatible?
Jupyter-compatible
Not Jupyter-compatible
Programming languages
Programming languages
Jupyter languages (e.g. Python, R)
JS
What kind of data sources can you connect to?
What kind of data sources can you connect to?
Connect with Jupyter libraries (e.g. SQLAlchemy, psycopg2)
Connect with JS libraries (e.g. REST APIs)
Connect to databases (MySQL, Postgres)
Connect to data warehouses (BigQuery, Snowflake)
Provided file storage
What kind of data visualization can you do?
What kind of data visualization can you do?
Jupyter data visualization (e.g. Matplotlib, Altair, Plotly)
JS data visualization (e.g. D3, Vega)
Reactivity
Reactivity
No reactivity, you decide the execution order
Full, realtime reactivity
Notebook scheduling
Notebook scheduling
Notebook scheduling with additional tools
No notebook scheduling
Management
Reproducibility
Reproducibility
With effort, you can make reproducible environments