If you’re using Jupyter just to run SQL queries against data files, you’re maintaining a Python environment, managing kernels, and installing packages for something that should be simpler. PondPilot gives you SQL on files without any of that.
The Jupyter Tax
To query a CSV with SQL in Jupyter, you need:
- A Python environment (or conda, or venv)
pip install duckdb pandas jupyter- A running Jupyter server
- A notebook with boilerplate code
With PondPilot, you need: a browser tab.
SQL-First, Not Code-First
PondPilot is built for SQL. You get a dedicated SQL editor with syntax highlighting, autocomplete, and a results grid. No %%sql magic commands, no pd.read_csv(), no conn.execute(). Just SQL.
SELECT category, SUM(amount) as total
FROM expenses.csv
GROUP BY category
ORDER BY total DESC;
Type it, run it, see results. That’s the entire workflow.
When PondPilot Wins
Ad-hoc analysis: You have a file and want answers fast. PondPilot is ready in seconds.
Non-Python users: Not everyone on your team knows Python. Everyone can learn SQL.
Zero maintenance: No packages to update, no kernel crashes, no “works on my machine” issues.
Privacy: PondPilot runs in your browser. Jupyter typically runs on a server (local or cloud).
When Jupyter Wins
Visualization: Jupyter with matplotlib/plotly is better if you need charts inline with your analysis.
Multi-language workflows: If you’re mixing SQL, Python, and R in one analysis, Jupyter’s flexibility is hard to beat.
Reproducible reports: Jupyter notebooks are shareable documents combining code, output, and narrative.
Use Both
Query data in PondPilot for quick exploration, export results, and load them into Jupyter when you need visualization or further Python processing. They complement each other.
Try PondPilot
Open PondPilot — SQL on files, zero setup.