JSON to Parquet Converter Online — Free JSON Array Tool

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How to JSON to Parquet Converter Online

1. 1

   Paste a JSON array (or newline-delimited NDJSON) into the input panel, or upload a .json file.

2. 2

   Select the compression codec: Snappy (fast, default), Gzip (high compression), Zstd (balanced), or Uncompressed.

3. 3

   Click "Convert" or press ⌘↵ to run the DuckDB WASM engine and generate the Parquet file in memory.

4. 4

   Review the inferred schema — column names and data types — displayed below the output.

5. 5

   Click "Download .parquet" to save the file locally for use with Spark, Athena, BigQuery, or DuckDB.

JSON to Parquet Converter Features

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  Powered by DuckDB WASM — a full analytical query engine running in your browser via WebAssembly

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  Supports standard JSON arrays and newline-delimited JSON (NDJSON / JSON Lines) input formats

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  Automatic schema inference: maps JSON types to Parquet types (INT32, INT64, DOUBLE, BOOLEAN, BYTE_ARRAY)

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  Three compression codecs: Snappy for fast read/write, Gzip for maximum compression, Zstd for a balanced trade-off

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  Displays inferred schema with column names and physical types before download

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  Shows output file size and compression ratio compared to the raw JSON input

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  Generates standard Parquet files compatible with Apache Spark, AWS Athena, Google BigQuery, and Pandas

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  Runs entirely in your browser using WebAssembly — your data never leaves your machine

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  No file size limit imposed by the tool (limited only by available browser memory)

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  Keyboard shortcut ⌘↵ to convert instantly

Frequently Asked Questions

What is Apache Parquet?

Apache Parquet is a columnar storage format widely used in big data ecosystems. Unlike row-oriented JSON or CSV, Parquet stores data column-by-column, enabling efficient compression and fast analytical queries that scan only the columns needed. It is the de facto standard for data lakes on AWS S3, Google Cloud Storage, and Azure Data Lake.

Why use Parquet instead of JSON?

Parquet offers significantly smaller file sizes (typically 5–10× smaller than JSON after compression), much faster query performance for analytical workloads, built-in schema enforcement, and efficient columnar compression. It is the preferred format for data warehouses and big data processing frameworks.

Which compression codec should I use?

Snappy is recommended for most use cases — it provides good compression ratios with very fast read and write speeds, making it ideal for interactive Spark/Athena queries. Use Gzip when storage cost is the primary concern. Zstd offers a good balance between size and speed and is increasingly preferred in modern pipelines.

How does DuckDB WASM work in the browser?

DuckDB is compiled to WebAssembly (WASM), allowing the full analytical SQL engine to run inside your browser tab without any server. When you click Convert, DuckDB reads your JSON, infers the schema, and writes a Parquet file into browser memory — all locally.

Are there file size limits?

The tool itself does not impose a size limit. Practical limits depend on your browser's available memory — typically 1–2 GB of JSON can be processed on a modern laptop. For very large datasets, use DuckDB CLI, Apache Arrow, or a Spark cluster instead.

Can I use the output with Spark, Athena, or BigQuery?

Yes. The generated Parquet files follow the Apache Parquet specification and are fully compatible with Apache Spark (`spark.read.parquet`), AWS Athena (point to S3 location), Google BigQuery (load job with Parquet format), Pandas (`pd.read_parquet`), and DuckDB (`read_parquet`).

Is my data safe to use here?

Yes. The entire conversion runs inside your browser via WebAssembly — no data is transmitted to any server. This makes it safe for sensitive or proprietary datasets, as nothing leaves your machine.

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