datacsv

Datacsv is a data interchange format and accompanying software library designed to improve the handling of CSV data by allowing optional metadata alongside the tabular rows. It is intended to be language- and platform-agnostic, enabling data producers and consumers to exchange structured information with minimal friction.

The core idea is to keep the familiar comma-separated values structure while providing a lightweight mechanism to describe column names, data types, and constraints. A datacsv file may include a metadata section at the top or an accompanying schema file, detailing field names, allowed value ranges, and encoding. The data portion remains compatible with standard CSV tooling, enabling filtering and transformation with existing utilities.

Datacsv supports streaming and chunked parsing to handle large datasets without loading them entirely into memory. It also emphasizes schema validation, allowing producers to enforce data quality and enabling consumers to validate records before processing.

Implementation exists in multiple languages, with libraries that offer read and write interfaces, validation helpers, and utilities for schema inference or explicit declarations. The format aims to interoperate with conventional CSV workflows while providing better data integrity in pipelines.

Criticism centers on fragmentation and the potential for compatibility gaps between implementations. Practitioners are advised to agree on a common metadata convention within teams to avoid misinterpretation.

See also: CSV, data interchange formats, data schema.