100% Browser-Side Processing
All parsing happens locally using WebAssembly. Your Parquet file never leaves your device — complete privacy guaranteed.
Upload your Apache Parquet file and instantly browse rows, inspect schema, filter columns, sort data and export — all in your browser. Your data never leaves your device.
or click to browse from your device
A professional-grade viewer built for data engineers, analysts and developers.
All parsing happens locally using WebAssembly. Your Parquet file never leaves your device — complete privacy guaranteed.
Reads large Parquet files in under a second using optimised columnar decoding — even files with millions of rows.
Instantly search across all columns or target a specific field. Results update as you type with zero delay.
Click any column header to sort ascending or descending. Multi-column sorting support keeps your analysis workflow smooth.
View every column name and its inferred data type — strings, integers, floats, booleans, timestamps and more — at a glance.
Download your filtered or full dataset as CSV or JSON with a single click. Perfect for downstream analysis in any tool.
Navigate large datasets effortlessly with smart pagination. Choose 25, 50, 100 or 250 rows per page to suit your workflow.
Instantly see row count, column count and file size metadata — essential for understanding your dataset at a glance.
Fully responsive design works seamlessly on smartphones and tablets. View Parquet files on the go without any compromise.
No installation, no account, no waiting. Just open and explore.
Drag and drop your .parquet file onto the upload zone, or click to open the file browser. Any Parquet file is accepted.
The schema panel instantly shows all column names and inferred data types so you understand your data structure immediately.
Use the search bar to find specific rows, click column headers to sort, and paginate through even the largest datasets.
Download the full or filtered dataset as CSV or JSON, or copy rows to clipboard for quick use in other applications.
Apache Parquet is an open-source, column-oriented file format widely adopted in big-data ecosystems
like Apache Spark, Hadoop, AWS Athena and Google BigQuery. While powerful for large-scale analytics,
its binary encoding means you cannot simply open it in a text editor or spreadsheet application —
you need a dedicated reader. Traditionally, viewing Parquet data required Python with pandas or
PyArrow, a Spark cluster, or a local database engine. That's a significant overhead for a quick
data inspection.
Our free Parquet Viewer Online eliminates that friction entirely. Using modern
WebAssembly technology compiled from the Apache Arrow C++ library, your browser parses the binary
Parquet format natively — no server upload, no software installation and no command-line knowledge
required. Simply drag your .parquet file into the viewer and your columnar data renders
immediately as a clean, sortable, searchable table. The schema inspector reveals every column's
data type, row counts tell you dataset size at a glance, and the export button lets you pull
filtered results as CSV or JSON in one click. Whether you're a data engineer validating a pipeline
output, an analyst previewing a dataset or a developer debugging an ETL job, this tool gives you
instant, private, zero-cost access to any Parquet file — right from your browser tab.
Prefer code? Here are the fastest ways to open Parquet files in popular languages.
# Install: pip install pandas pyarrow import pandas as pd # Load the parquet file df = pd.read_parquet('data.parquet') # Preview first 10 rows print(df.head(10)) # Show column types print(df.dtypes) # Export to CSV df.to_csv('output.csv', index=False)
# Install: pip install duckdb import duckdb # Query Parquet directly with SQL — no loading needed result = duckdb.sql(""" SELECT * FROM 'data.parquet' WHERE column_name = 'value' LIMIT 100 """).fetchdf() print(result) # Describe schema duckdb.sql("DESCRIBE SELECT * FROM 'data.parquet'").show()
// Install: npm install parquet-wasm import { readParquet } from 'parquet-wasm'; import { tableFromIPC } from 'apache-arrow'; const buf = await fs.promises.readFile('data.parquet'); const arrowIPC = readParquet(new Uint8Array(buf)); const table = tableFromIPC(arrowIPC.intoIPCStream()); console.log(`Rows: ${table.numRows}, Cols: ${table.numCols}`); console.log(table.toString());
# Install: install.packages("arrow") library(arrow) # Read Parquet file df <- read_parquet("data.parquet") # Inspect head(df, 10) str(df) # Export to CSV write.csv(df, "output.csv", row.names = FALSE)
Everything you need to know about viewing Parquet files online.
Join thousands of data professionals who trust our free online viewer for fast, private Parquet file inspection.