Parquet Viewer
View and inspect Apache Parquet columnar data files with metadata preview
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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
- File Validation Verify your Parquet file has valid magic bytes and structure
- Metadata Display View file size and basic metadata information
- 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?
Currently, the viewer validates files and shows basic metadata. For full data preview with schema and column statistics, 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.