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.
Select a Parquet file to analyze and query. Maximum file size: 100MB
Drop your Parquet file here
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Supported formats: .parquet, .parq
Max size: 100 MB
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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())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)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)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();
}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);
}
}Take control of your data - everything in one place
Upload a Parquet file (.parquet) using the file selector
Review the schema to understand columns, data types, and nesting
Inspect metadata such as compression, encoding, and row groups
Browse records in a tabular view for validation or debugging
Apply filters or search to focus on specific data
Export selected data to JSON or CSV if required
| Feature | Online JSON Formatt(OJF) | Other Tools |
|---|---|---|
| Schema visibility | Hierarchical schema with metadata context | Limited or flat schema display |
| Browser-based processing | Local, client-side inspection | Server upload required |
| Export flexibility | JSON and CSV export | Restricted or no export options |