Online Parquet Viewer & Data Inspector
Open, inspect, and analyze Apache Parquet (.parquet) columnar files directly in your web browser. Schema column type audits, tabular data preview, CSV export, and zero cloud uploads.
| transaction_id | customer_name | sku_code | quantity | unit_price_usd | timestamp |
|---|---|---|---|---|---|
| 1008491 | Global Corp | SKU-ULTRA-01 | 5 | 1200.5 | 2026-08-20 04:15:00 |
| 1008492 | Apex Retail | SKU-STD-09 | 24 | 45 | 2026-08-20 04:22:15 |
| 1008493 | Nexus Labs | SKU-PRO-03 | 2 | 3400 | 2026-08-20 05:01:40 |
| 1008494 | Vortex AI | SKU-ULTRA-01 | 10 | 1200.5 | 2026-08-20 05:30:10 |
| 1008495 | HyperCloud | SKU-SERV-88 | 1 | 9500 | 2026-08-20 06:10:00 |
| 1008496 | CyberTech | SKU-STD-09 | 15 | 45 | 2026-08-20 06:45:30 |
The Complete Guide to Apache Parquet & Columnar Big Data Processing
Apache Parquet is an open-source, columnar storage file format optimized for fast analytical queries across modern data lakes, data warehouses, and distributed computing frameworks (Apache Spark, Snowflake, Databricks, DuckDB, AWS Athena, Google BigQuery). Unlike traditional row-based formats (CSV, JSON), Parquet stores data column-by-column rather than row-by-row.
Columnar organization enables homogeneous data types to be compressed using advanced algorithms (Run Length Encoding, Bit Packing, Dictionary Encoding, Snappy), shrinking storage footprints by up to 80% while accelerating query performance. The HiFi Toolkit Online Parquet Viewer provides an instant in-browser GUI to inspect schemas and browse data rows effortlessly.
Parquet vs. CSV vs. JSON Comparison
| Attribute | Apache Parquet | CSV File | JSON Lines |
|---|---|---|---|
| Storage Layout | Columnar (Optimized for Analytics) | Row-based | Row-based |
| Data Typing | Strict Typed Schema | Untyped Text Strings | Loose JSON Types |
| Compression Ratio | Very High (Snappy / ZSTD) | None / Raw text | High overhead |
How to View Parquet Files Online
- Upload File: Click Open Parquet File or drag and drop any
.parquetfile. - Inspect Schema: Review column definitions, types (INT64, UTF8, DOUBLE), and row group counts.
- Export to CSV: Click Export to CSV to download a portable spreadsheet version.
