Databricks Notebooks vs Jupyter: a side-by-side comparison for 2024
Comparing two data science notebooks.
Databricks Notebooks
Collaborate across engineering, data science, and machine learning teams with support for multiple languages, built-in data visualizations, automatic versioning, and operationalization with jobs.
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.
Databricks Notebooks
Jupyter
Setup
Is it managed?
Is it managed?
Fully managed (setup in minutes)
No, you must host it yourself
Can you self-host?
Can you self-host?
You can self-host (setup in hours)
You can self-host (setup in hours)
Features
Is it Jupyter compatible?
Is it Jupyter compatible?
Jupyter-compatible
Jupyter-compatible
Programming languages
Programming languages
Jupyter languages (e.g. Python, R)
Jupyter languages (e.g. Python, R)
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 to data warehouses (Databricks)
Connect with Jupyter libraries (e.g. SQLAlchemy, psycopg2)
What kind of data visualization can you do?
What kind of data visualization can you do?
Jupyter data visualization (e.g. Matplotlib, Altair, Plotly)
UI for building charts
Jupyter data visualization (e.g. Matplotlib, Altair, Plotly)
Reactivity
Reactivity
No reactivity, you decide the execution order
No reactivity, you decide the execution order
Notebook scheduling
Notebook scheduling
Notebook scheduling is built in
Notebook scheduling with additional tools
Management
Reproducibility
Reproducibility
Environments are reproducible by default
With effort, you can make reproducible environments