Agents for Data

Parquet SQL Query Tool

Execute SQL queries on Parquet files using DuckDB. Upload your file and start querying your data with full SQL support.

Load Parquet File
Upload a Parquet file to start querying with SQL, or try with sample data
Click anywhere to select a fileor drag and drop a file here
Accepts Parquet file

Powerful SQL Querying

DuckDB Powered
Uses DuckDB WASM for fast, in-browser SQL processing with full analytics capabilities.
SQL Editor
Syntax-highlighted SQL editor with autocompletion and error detection.
Instant Execution
Execute queries instantly in your browser without server roundtrips.
Export Results
Download query results as CSV files for further analysis.
Example SQL Queries
Click any query below to copy it to the SQL editor

Basic Selection

Count Records

Column Summary

Aggregation

How to query Parquet with SQL online

  1. Upload your Parquet file using the file upload area above
  2. Your data will be automatically loaded into a table called "data"
  3. Write SQL queries in the editor to explore and analyze your data
  4. Click "Execute Query" to run your SQL and see results
  5. Use the download button to export query results as CSV
  6. Try different queries to filter, sort, aggregate, and transform your data

Powered by DuckDB: Our SQL engine supports advanced analytics functions, window functions, and most PostgreSQL-compatible SQL syntax. Perfect for data analysis and exploration.

Common SQL queries for Parquet data:

sql
-- Basic data exploration
SELECT * FROM data LIMIT 10;
sql
-- Count total rows
SELECT COUNT(*) as total_rows FROM data;
sql
-- Get column information
DESCRIBE data;
sql
-- Basic filtering
SELECT * FROM data WHERE column_name = 'value';
sql
-- Aggregation and grouping
SELECT column_name, COUNT(*), AVG(numeric_column)
FROM data 
GROUP BY column_name;
sql
-- Sorting results
SELECT * FROM data 
ORDER BY column_name DESC 
LIMIT 20;
sql
-- Parquet files support efficient columnar operations
SELECT column1, column2, COUNT(*) 
FROM data 
WHERE date_column >= '2024-01-01'
GROUP BY column1, column2;

Table name: Your data is automatically loaded into a table called data

File formats supported: CSV, TSV, JSON, and Parquet files

No installation required: Everything runs directly in your browser using WebAssembly