Parquetly

A fast, privacy-focused Parquet file viewer that runs entirely in your browser. Explore schemas, query data with SQL, and navigate large datasets without uploading files to any server.

✨ Features

• 🔒 100% Private: All data processing happens in your browser. Your files never leave your computer.
• 🔍 Schema Inspection: View column names, types, and nullability at a glance
• 📊 Data Preview: Browse through your data with pagination
• 🗂️ Row Group Analysis: Inspect Parquet row group metadata and statistics
• 💾 SQL Querying: Run SQL queries against your data using DuckDB-WASM
• ⚡ Fast & Lightweight: Built with modern web technologies for optimal performance
• 🎨 Clean UI: Intuitive interface built with shadcn/ui components

🚀 Quick Start

Try It Online

Visit parquetly.com or click "Try with a sample file" to explore a Law Stack Exchange dataset.

Run Locally

# Clone the repository
git clone https://github.com/Gilbert09/parquetly.git
cd parquetly

# Install dependencies
npm install

# Start the development server
npm run dev

🛠️ Tech Stack

• Frontend: React + TypeScript + Vite
• UI Components: shadcn/ui + Tailwind CSS
• Parquet Reading: parquet-wasm
• SQL Engine: DuckDB-WASM
• Analytics: PostHog

📖 Usage

1. Upload a File: Drag and drop a .parquet file or click to browse
2. Explore the Data:
   • Data Tab: Browse rows with pagination
   • Schema Tab: View column definitions and types
   • Row Groups Tab: Inspect Parquet file metadata
   • Query Tab: Write SQL queries to analyze your data

SQL Querying

Your Parquet data is automatically loaded into a DuckDB table named data. You can query it using standard SQL:

-- Get row count
SELECT COUNT(*) FROM data;

-- Filter and aggregate
SELECT category, COUNT(*) as count, AVG(score) as avg_score
FROM data
WHERE score > 10
GROUP BY category
ORDER BY count DESC;

-- DuckDB's powerful string functions
SELECT
  REGEXP_EXTRACT(email, '([^@]+)@', 1) as username,
  STRING_SPLIT(tags, ',') as tag_array
FROM data
WHERE email LIKE '%@gmail.com';

-- Advanced analytics with window functions
SELECT
  date,
  value,
  AVG(value) OVER (ORDER BY date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) as moving_avg_7day,
  PERCENT_RANK() OVER (ORDER BY value) as percentile
FROM data;

-- JSON operations (if your parquet has JSON columns)
SELECT
  json_extract(metadata, '$.user.name') as user_name,
  json_extract_string(settings, '$.theme') as theme
FROM data;

-- Date/time manipulation
SELECT
  date_trunc('month', timestamp) as month,
  COUNT(*) as events,
  DATE_DIFF('day', MIN(timestamp), MAX(timestamp)) as day_span
FROM data
GROUP BY month;

Press Cmd/Ctrl + Enter to execute queries.

🔐 Privacy & Security

Parquetly is designed with privacy in mind:

• ✅ Zero server uploads - all processing is client-side
• ✅ No data storage - files are only held in browser memory
• ✅ No tracking of file contents
• ✅ Open source - audit the code yourself

📦 Building for Production

npm run build

The built files will be in the dist directory, ready to deploy to any static hosting service.

🤝 Contributing

Contributions are welcome! Feel free to open issues or submit pull requests.