JSON vs Parquet

JSON (JavaScript Object Notation) is a plain-text format supporting objects, arrays, nested structures, and native data types — strings, numbers, booleans, null, arrays, and nested objects. It is the standard format for REST APIs, NoSQL document databases, configuration files, and structured application logs. JSON is human-readable and supported by every programming language and web platform.

JSON's flexibility is its defining characteristic: a JSON document can have variable structure, deeply nested fields, and arrays of objects within objects. This makes JSON ideal for data that does not fit a rigid tabular schema. The trade-off is verbosity — in a large JSON array, every record repeats every key name, making JSON far larger than equivalent columnar formats.

Apache Parquet is an open-source binary columnar storage format. It stores data column by column, embeds the column schema in the file footer, and applies efficient compression codecs like Snappy or Zstandard. A JSON dataset converted to Parquet typically shrinks to 5–15% of its original size. Parquet is the native format of AWS Athena, Google BigQuery, Apache Spark, and most cloud data warehouse platforms.

Parquet's columnar layout enables a critical performance optimisation: a query that reads only three of a table's twenty columns scans roughly 15% of the file. On a dataset with billions of rows, this difference in I/O cost translates directly into query cost and latency.