dataformats

Data formats are conventions for encoding and organizing information so it can be stored, transmitted, and interpreted by software. A data format defines the syntax for representing values, the rules for combining them, and often how metadata and schema are included. Formats can be human-readable or binary, and may emphasize readability, efficiency, or both. The choice of format affects interoperability, processing speed, and storage requirements.

Data formats are commonly categorized as text-based or binary. Text-based formats include CSV, JSON, XML, YAML, INI, and TOML, which are easy to inspect and edit with simple tools. Binary formats such as Parquet, Avro, Protobuf, and MessagePack prioritize compactness and fast parsing, often at the cost of human readability. Formats also differ in structure, ranging from flat (CSV) to hierarchical (XML, JSON) to columnar (Parquet) or row-oriented encodings (Some database-specific formats).

Schemas and validation play a central role in many formats. Self-describing formats (XML, JSON) embed structure in the data itself, while others rely on separate schema definitions (XML Schema, JSON Schema, Protobuf .proto files) to enforce data types and guide evolution. Validation and versioning help maintain backward compatibility as data models change.

Common considerations when selecting a format include character encoding (typically UTF-8), compression, portability across systems, and the cost of serialization and deserialization. Practical choices balance human accessibility, performance, and ecosystem support, depending on use cases such as data interchange, configuration, logging, or analytical storage.