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Parquet vs JSON
Parquet is a binary columnar format for analytics; JSON is a text format for structured-data exchange. Parquet is often used for large analytical datasets, while JSON is common in APIs and configuration.
Which format should you choose?
Consider Parquet when an analytics system repeatedly reads selected columns from large datasets. Choose JSON for APIs or exchanges where a readable text representation is useful.
Good fit for Parquet
Store structured tables for analytics
- Large datasets read by selected columns
Good fit for JSON
Web APIs, configuration files, and data exchange
- Transferring structured data
Basic information
| Field | Parquet | JSON |
|---|---|---|
| Extension | .parquet | .json |
| MIME type | application/vnd.apache.parquet | application/json |
| Common uses | Store structured tables for analytics | Web APIs, configuration files, and data exchange |
| How to open | Use a Parquet-aware tool to inspect schema, column types, row counts, and values. | Open it in a text editor, IDE, or JSON-aware viewer. |
| Export or convert | From CSV or other formats, check types, nulls, timestamps, encoding, partitions, and row counts. | Applications or conversion tools can convert it to formats such as CSV. |
Choose by use case
Large datasets read by selected columns
Store structured tables for analytics
ParquetTransferring structured data
Web APIs, configuration files, and data exchange
JSONKey point 1
Parquet stores data by column and uses schema and column chunks to support analytical reads.
More on the differencesKey point 2
JSON represents objects and arrays as text and is widely used for APIs and configuration files.
More on the differencesMore on the differences
Parquet usually needs a compatible library or analytics tool, while JSON can be inspected as text. For large JSON workloads, measure storage and parsing costs.
- Conversion can change how types, nulls, timestamps, numeric precision, and nested structures are represented.
- Choose according to the access pattern and receiving tools, not on the assumption that either format is universally smaller or faster.
Frequently asked questions
Can JSON replace Parquet for analytics?
It can work for some workloads, but Parquet is designed for column-oriented analytical reads. Test storage, query performance, and tool support with representative data.