Parquet is the go-to columnar format for analytics, but querying it usually means spinning up Python, Spark, or a database. PondPilot lets you query Parquet files directly in your browser with SQL.
Zero Setup Parquet Querying
No pip install, no import pandas, no Jupyter kernel restarts. Open app.pondpilot.io, drop a .parquet file, and run SQL immediately.
DuckDB’s native Parquet support means you get predicate pushdown and column pruning for free — even in the browser. Queries on large Parquet files are fast because DuckDB only reads the columns and row groups you actually need.
Inspect Schema Instantly
Not sure what’s in that Parquet file someone sent you? PondPilot shows you the schema — column names, types, and row counts — the moment you open it. No guessing, no parquet-tools CLI.
DESCRIBE SELECT * FROM sales_2024.parquet;
Join Parquet with CSV
Got a Parquet data export and a CSV lookup table? Open both in PondPilot and join them:
SELECT s.*, r.region_name
FROM sales_2024.parquet s
JOIN regions.csv r ON s.region_id = r.id;
Mix and match file formats in the same query session.
Export Results
Query results can be exported back to CSV for sharing with colleagues who prefer spreadsheets. Run your analysis in SQL, share the output in whatever format works.
Privacy First
Your Parquet files stay on your machine. PondPilot has zero backend — no server receives your data, ever. This matters when you’re working with production data exports or sensitive datasets.
Try It Now
Open PondPilot and query your first Parquet file. Free, open source, no account required.