Parquet to JSON
Convert Parquet-sourced data to JSON by uploading a CSV file exported from a Parquet dataset using pandas, DuckDB, or any similar tool — the converter handles header detection, type coercion for numbers and booleans, and pretty-printing automatically. Toggle whether the first row contains column headers and choose between compact or formatted JSON output to suit your downstream use case. The entire conversion runs client-side in your browser, so no data leaves your machine.
How to use with .parquet files
True .parquet files cannot be parsed in the browser. Export your Parquet data to CSV first, then upload here:
# Python / pandas
import pandas as pd
pd.read_parquet("data.parquet").to_csv("data.csv", index=False)
# DuckDB
COPY (SELECT * FROM 'data.parquet') TO 'data.csv' (HEADER, DELIMITER ',');CSV file (from Parquet export)
Drag and drop a CSV file here, or click to browse
Accepts .csv files exported from Parquet
JSON
What is Parquet to JSON Converter?
Parquet is a binary format — you cannot open it in a text editor or paste it into a tool. But Parquet data regularly lands in your lap as a CSV export from DuckDB, pandas, AWS Athena, or BigQuery, and you need to work with it in JSON-native tools. This converter takes that CSV export and produces a properly typed JSON array: numeric columns become JSON numbers, boolean columns become JSON booleans, and dot-notation column names from flattened Parquet schemas are optionally reconstructed into nested JSON objects that match the original hierarchical structure. It is the bridge between the columnar analytics world and the document-oriented JSON ecosystem — useful for loading Parquet-sourced data into MongoDB, testing JSON processing logic against real analytics data, or inspecting the schema of a data lake table without a full query engine.
How to Use
- 1
Upload Your Parquet CSV Export
Parquet files are binary — this tool processes CSV exports from Parquet readers (DuckDB, pandas, Spark, AWS Athena). Paste the CSV export or upload the .csv file to begin.
- 2
Configure Column Parsing
Set whether to infer numeric types from string columns, how to handle null/NaN values, and whether to reconstruct nested objects from dot-notation column names (e.g., "user.city" → nested JSON).
- 3
Convert to JSON Array
Click "Convert to JSON". Each CSV row becomes a JSON object; dot-notation column headers are optionally reconstructed into nested JSON structures matching the original Parquet schema.
- 4
Copy or Download the JSON
Copy the JSON array for use in Python, Node.js, or jq pipelines — or download it as a .json file for loading into MongoDB, Elasticsearch, or other JSON-native data stores.
Common Use Cases
Data Lake Inspection
Convert Parquet CSV exports from data lakes (AWS S3, Azure Data Lake, Google Cloud Storage) into JSON to inspect schemas, validate row counts, and preview data without Spark or Hive.
Python & Node.js Pipeline Integration
Transform Parquet-exported CSV data into JSON arrays for consumption by Python (pandas, FastAPI) or Node.js services that work natively with JSON rather than binary columnar formats.
BI Tool Data Bridging
When extracting data from columnar stores for use in lightweight dashboards or ad-hoc analysis, convert Parquet exports through CSV to JSON as an intermediate step before loading into visualization tools.
Schema Validation & Debugging
Convert a Parquet export sample to JSON to quickly check field names, data types, and null values without needing a full Parquet reader library or cloud environment access.
Conversion Examples
Parquet CSV Export → JSON Array
Parquet data exported as CSV is converted to a typed JSON array.
Input JSON
id,name,timestamp,value 1,sensor_A,2024-01-01T00:00:00Z,23.5 2,sensor_B,2024-01-01T00:01:00Z,24.1
Output CSV
[
{"id": 1, "name": "sensor_A", "timestamp": "2024-01-01T00:00:00Z", "value": 23.5},
{"id": 2, "name": "sensor_B", "timestamp": "2024-01-01T00:01:00Z", "value": 24.1}
]Columnar Data → Nested JSON
Dot-notation column names from flattened Parquet schemas are reconstructed into nested JSON.
Input JSON
user_id,user_name,address_city,address_zip 1,Alice,London,EC1A 2,Bob,Paris,75001
Output CSV
[
{"user_id": 1, "user_name": "Alice", "address": {"city": "London", "zip": "EC1A"}},
{"user_id": 2, "user_name": "Bob", "address": {"city": "Paris", "zip": "75001"}}
]