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Specifications

The Data Package Standard is a comprehensive set of specifications that collectively define a framework for organizing, documenting, and sharing data in a structured and interoperable manner. It comprises four key components, each serving a specific purpose in the data management process:

Data Package

Purpose: The Data Package serves as the central container for datasets, offering a high-level view of data contents and metadata.

Specifications: Data Packages are defined by a set of required files, including a descriptor file (datapackage.json), data files, and optional resources.

Functions: Data Packages simplify data distribution and discovery by packaging data with essential metadata, such as data sources, licensing, and schema information.

Data Resource

Purpose: Data Resources represent individual data files or tables within a Data Package, allowing for the organization of distinct data segments.

Specifications: Each Data Resource is described in a descriptor file (datapackage.json) under the “resources” property, providing details about data location, schema, and additional metadata.

Functions: Data Resources enable the partitioning of large datasets into manageable units and maintain clear organization within Data Packages.

Table Dialect

Purpose: Table Dialects specify the format and characteristics of tabular data within Data Resources, accommodating various formats like CSV, Excel, or JSON.

Specifications: Table Dialect definitions detail data structure, including delimiter characters, headers, and other format-specific properties.

Functions: Table Dialects ensure accurate interpretation of tabular data by software tools, promoting data consistency and interoperability.

Table Schema

Purpose: Table Schemas define the structure of data tables, specifying column names, types, and constraints to create a clear schema for tabular data.

Specifications: As a part of a Data Package or as an independent JSON descriptor, Table Schemas provide detailed information about table structure and column characteristics.

Functions: Table Schemas enhance data quality and consistency by specifying expected column formats and properties, supporting data validation and integration into analysis tools.