CSVJSONParquet

CSVJSONParquet is a term used to describe the interoperability between the CSV, JSON, and Parquet data formats. In practice, it refers to workflows and tooling that convert data among these representations, enabling data engineers to move data between simple text formats and efficient binary columnar storage.

CSV is a plain text format that uses a delimiter, typically a comma, to separate fields. It is human-readable and easy to generate, but provides no inherent data typing and can be affected by escaping rules or multiline fields.

JSON is a text-based, hierarchical data format that represents objects and arrays. It supports complex structures and optional schemas, but can be verbose and slower to parse for large datasets.

Parquet is a binary, columnar storage format optimized for analytical workloads. It stores data column-wise, supports compression and encoding, and enables efficient queries through predicate pushdown and column pruning. Parquet supports nested structures and schema evolution.

Typical workflows include: converting CSV to Parquet for analytics in data lakes, using libraries like Apache Spark, Pandas with PyArrow, or dedicated conversion tools; converting JSON to Parquet to enable fast queries; exporting Parquet data back to CSV or JSON for interoperability.

Challenges and considerations include mapping data types between formats (for example, strings vs numbers), handling missing values, dealing with nested JSON, and preserving schema when moving between formats. CSV has no native schema; JSON may have inconsistent structures; Parquet requires a defined schema but can evolve.

Usage context: data warehousing, ETL pipelines, data lakes, and cross-system data interchange. While CSV and JSON are human-readable and easy to produce, Parquet offers performance and storage efficiency for large-scale analytics.