JSON to Parquet for Tick Data, Without Writing Python

Feeds and exports often arrive as JSON. Parquet is a better format to keep and query. Here is how to convert one to the other.

Open the JSON to Parquet converter

Quant developers and researchers often receive market data feeds and exports as JSON: one object per trade or quote, with a timestamp, an instrument, a price and a size. JSON is easy to send but heavy to store. Every record repeats every field name, numbers are text until parsed, and the whole file must be read to answer any question.

Parquet stores the same records by column, with a type for each. Repeated values such as an instrument code compress well, and tools can read only the columns they need. That is why teams convert JSON feeds to Parquet before analysis.

The Python route is pandas.read_json followed by to_parquet, and it is fine when you are already in a notebook. For a one-off file, a quick check or a colleague without Python, the Format Converter does it in the browser: choose JSON as the source and Parquet as the target, drop the file and download the result. Nested objects are flattened to columns with their path as the name, and numbers, dates and true or false values are detected.

Check the result. Open the Parquet file again with the Format Converter or a tool such as DuckDB and confirm the row count matches the source. Check that timestamps were read as dates and not left as text, and that identifiers were not turned into numbers. If a field is sometimes missing, expect empty cells for those rows.

The file is processed in your browser and is not uploaded. Very large files are limited by your device memory, and the site offers a large-file mode for files of 500 MB and more. Nothing here changes the content of your data, and the example is about file handling, not trading.

Open the JSON to Parquet converter

Related tool pages

  • JSON, CSV, Parquet and Excel Converter: Convert between JSON, CSV, Parquet and Excel online for free, one file or a whole folder. It runs in your browser, so nothing is uploaded.
  • JSON to CSV Converter: Convert JSON to CSV in seconds. Drop a file or paste data and download the result. Private: it runs in your browser and nothing is uploaded. Large files too.
  • CSV to JSON Converter: Convert CSV to JSON in seconds. Drop a file or paste data and download the result. Private: it runs in your browser and nothing is uploaded. Large files too.
  • CSV to Parquet Converter: Convert CSV to Parquet in seconds. Drop a file or paste data and download the result. Private: it runs in your browser and nothing is uploaded. Large files too.
  • Parquet to CSV Converter: Convert Parquet to CSV in seconds. Drop a file or paste data and download the result. Private: it runs in your browser and nothing is uploaded. Large files too.
  • Parquet to JSON Converter: Convert Parquet to JSON in seconds. Drop a file and download the result. Private: it runs in your browser and nothing is uploaded. Large files too.

How to use the JSON to Parquet converter: step by step

  1. Open the Format Converter and choose JSON to Parquet.
  2. Drop in your JSON file.
  3. Convert and download the Parquet file.
  4. Open it again and check the row count and the types.

When to use the JSON to Parquet converter

  • Convert a JSON export of trades or quotes to Parquet
  • Shrink a JSON archive before storing it
  • Prepare data for DuckDB or pandas
  • Check a feed sample without writing a script

Example

Input: [{"id":1,"name":"Ada","age":36},{"id":2,"name":"Alan","age":41}]

Output: A Parquet file with the same columns (id, name, age) and typed values.

Frequently asked questions

How do I convert JSON to Parquet?

Drop your JSON file (or paste the data), check the options, click Convert and download the Parquet result.

How big can a file be?

It depends on your device. A few hundred MB usually works.

Which conversions does the Format Converter support?

Any direction between JSON, CSV, Parquet and Excel (.xlsx), for one file or a whole folder. Nested JSON is flattened into columns such as address.city. For Excel, the first sheet is read; old .xls files are not supported.

Why did my number or date change after converting?

Types are detected from the values. Turn off "Detect types" to keep everything as text, which also keeps leading zeros in IDs and phone numbers.

Tip: If a file holds one record per line (NDJSON), use the large-file mode for big files.