Hyperquery
Hyperquery is a data notebook that enables you to easily build shareable analyses in SQL and Python.
Hyperquery vs Zeppelin:
a side-by-side comparison for 2024
Comparing two data science notebooks.
Zeppelin
Web-based notebook that enables data-driven, interactive data analytics and collaborative documents with SQL, Scala and more.
Hyperquery
Zeppelin
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?
No, you must use a managed offering
You can self-host (setup in hours)
Features
Is it Jupyter compatible?
Is it Jupyter compatible?
Not Jupyter-compatible
Not Jupyter-compatible
Programming languages
Programming languages
Python
SQL
Python
SQL
Spark
What kind of data sources can you connect to?
What kind of data sources can you connect to?
Connect to data warehouses (AWS, GCP, etc.)
Connect to databases (Postgres, MS SQL, etc.)
Connect with Jupyter libraries (e.g. SQLAlchemy, psycopg2)
What kind of data visualization can you do?
What kind of data visualization can you do?
UI for building charts
Jupyter data visualization (e.g. Matplotlib, Altair, Plotly)
Unknown
Reactivity
Reactivity
No reactivity, you decide the execution order
No reactivity, you decide the execution order
Notebook scheduling
Notebook scheduling
No notebook scheduling
Notebook scheduling is built in
Management
Reproducibility
Reproducibility
Environments are reproducible by default
With effort, you can make reproducible environments
Version history
Version history
Version history is built in
File-based (use Git)
Collaborative editing
Collaborative editing
Multiple editors at the same time
File-based (use Git)
Multiple editors, one at a time (asynchronous)
Comments
Comments
Comment on items within a notebook
File-based (use GitHub)
Notebook organization
Notebook organization
View notebooks in a tree, like a wiki
File-based
Licensing
License
License
Proprietary
Open-source (Apache 2.0)
Price
Price
Free tier
Pay-per-user
Free