Parquet Viewer
View and inspect Apache Parquet columnar data files with metadata preview
What is Apache Parquet?
Apache Parquet is a columnar storage file format designed for efficient data storage and retrieval. It provides efficient data compression and encoding schemes with enhanced performance to handle complex data in bulk.
Parquet files are commonly used in:
- Big data processing (Spark, Hadoop)
- Data lakes and warehouses
- Analytics and business intelligence
- Machine learning pipelines
- Data science workflows
Parquet Viewer Features
- Data Preview View actual row data with full column decoding
- Schema Inspection View column names, types, and file metadata
- Large File Support Handle Parquet files up to 100MB
- 100% Private Files processed entirely in your browser
Parquet Viewer FAQ
Why use Parquet instead of CSV?
Parquet offers significant advantages: 50-90% smaller file sizes due to compression, faster query performance with columnar storage, schema enforcement, and better support for complex data types.
Can I see the full data?
Yes! The viewer decodes and displays up to 1,000 rows of data directly in your browser. For larger datasets, consider using tools like pandas, DuckDB, or Apache Arrow.
What compression formats are supported?
Parquet files commonly use Snappy, Gzip, or LZ4 compression. Our validator detects valid Parquet files regardless of the internal compression used.
How do I create Parquet files?
You can create Parquet files using Python (pandas, pyarrow), Apache Spark, or many other big data tools. Example: df.to_parquet('file.parquet') in pandas.