Free Apache Parquet Viewer Online – Open & Explore Parquet Files

View and explore Apache Parquet files directly in your browser. Inspect schemas, metadata, and columnar data structures to better understand datasets used in analytics, data engineering, and ETL workflows or convert Parquet files to JSON, CSV, TSV, or XLSX for downstream processing.

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Select a Parquet file to analyze and query. Maximum file size: 250MB

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Supported formats: .parquet, .parq
Max size: 250 MB

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Online Parquet Viewer for Developers and Data Engineers

Open and inspect Apache Parquet files online with a browser-based viewer. Analyze columnar schemas, review metadata, explore records, and export subsets to JSON or CSV for debugging, validation, and data engineering workflows.

How to view Parquet files in Python

Viewing Parquet files with Pandas

python

Pandas is the most common library for data manipulation in Python and provide simple read_parquet function.

# Installation: pip install pandas
import pandas as pd

df = pd.read_parquet('path/to/file.parquet')
print(df.head())

Viewing Parquet files with DuckDB

python

DuckDB is an in-process SQL OLAP database management system that can query Parquet files directly.

# Installation: pip install duckdb
import duckdb

# Query directly from the file
result = duckdb.sql("SELECT * FROM 'path/to/file.parquet' LIMIT 10").fetchdf()
print(result)

Viewing Parquet files with ClickHouse

python

ClickHouse is a fast open-source OLAP database management system with native Parquet support.

# Installation: pip install clickhouse-connect
import clickhouse_connect

client = clickhouse_connect.get_client(host='localhost', port=8123)
query = "SELECT * FROM file('path/to/file.parquet') LIMIT 10"
result = client.query(query).result_set
print(result)

How to view Parquet files in other languages

Viewing Parquet in Node.js

javascript

Using the parquetjs library to read Parquet files in a JavaScript environment.

// Installation: npm install parquetjs
const parquet = require('parquetjs');

async function readParquet() {
    let reader = await parquet.ParquetReader.openFile('example.parquet');
    let cursor = reader.getCursor();
    let record = null;
    while (record = await cursor.next()) {
        console.log(record);
    }
    await reader.close();
}

Viewing Parquet in Java

java

Using Apache Parquet with Avro for record-level access in Java.

// Required dependencies: parquet-avro, parquet-hadoop
import org.apache.hadoop.fs.Path;
import org.apache.parquet.hadoop.ParquetReader;
import org.apache.parquet.avro.AvroParquetReader;
import org.apache.avro.generic.GenericRecord;

Path path = new Path("example.parquet");
try (ParquetReader<GenericRecord> reader = AvroParquetReader.<GenericRecord>builder(path).build()) {
    GenericRecord record;
    while ((record = reader.read()) != null) {
        System.out.println(record);
    }
}

Key Features of OJF

Take control of your data - everything in one place

Schema & Metadata Inspection

  • Inspect hierarchical Parquet schemas including nested fields and logical data types
  • Review column-level metadata such as nullability, encoding, and compression formats
  • Analyze row group information to understand data partitioning and layout
  • Examine file-level statistics used in analytical and data lake workloads

Data Exploration

  • Browse Parquet records using a structured tabular view for clear data analysis
  • Apply column-based filtering with built-in controls or SQL-style expressions
  • Sort records using data type–aware column sorting for accurate ordering
  • Convert epoch timestamp values into readable date and time formats
  • Statistical analysis of column data including min, max, and null value counts

Conversion & Export

  • Convert Parquet files into JSON format for APIs and application workflows
  • Export datasets as CSV or TSV for reporting and spreadsheet analysis
  • Download data in XLSX format for compatibility with Excel-based tools
  • Extract filtered or sorted subsets for validation and downstream processing
  • Download filtered datasets based on custom selection criteria

How to View Parquet Files Online

1

Upload a Parquet file (.parquet) using the file selector

2

Review the schema to understand columns, data types, and nesting

3

Inspect metadata such as compression, encoding, and row groups

4

Browse records in a tabular view for validation or debugging

5

Apply filters or search to focus on specific data

6

Export selected data to JSON or CSV if required

Why Choose Us?

Feature
Online JSON Formatt(OJF)
Other Tools
Schema visibilityHierarchical schema with metadata contextLimited or flat schema display
Browser-based processingLocal, client-side inspectionServer upload required
Export flexibilityJSON and CSV exportRestricted or no export options

Frequently Asked Questions

What is an Apache Parquet file?
Apache Parquet is an open-source columnar storage format designed for efficient data processing and analytics.Reference: Apache Parquet Documentation
Why is Parquet commonly used in data engineering?
Parquet is optimized for analytical workloads because it supports column pruning, compression, and efficient scans, making it widely used in big data and cloud analytics.Reference: Databricks – What is Parquet?
How can I view Parquet files without Apache Spark or Hadoop?
You can use an online Parquet viewer to inspect schemas, metadata, and records directly in the browser, without setting up Spark, Hadoop, or other big data frameworks.
What metadata can be inspected in a Parquet file?
Parquet metadata includes schema definitions, row groups, column statistics, compression codecs, and encoding types, which help understand performance and storage characteristics.Reference: Apache Parquet Format – File Metadata
Can Parquet data be converted to other formats?
Yes, Parquet data is often converted to JSON or CSV for interoperability, reporting, and debugging purposes.Reference: AWS Big Data Blog – Parquet Use Cases
Is Parquet suitable for large datasets?
Parquet is specifically designed for large-scale analytical datasets, enabling efficient storage, compression, and query performance across distributed systems.