datacur8

Table of contents

1. What is datacur8?
2. Key Features
3. Quick Start

What is datacur8?

datacur8 is a config-driven command-line tool that validates, exports, and tidies structured data files (JSON, YAML, and CSV) stored in a repository. It brings database-level data integrity to file-based datasets managed in git.

The problem: Teams often manage configuration, catalog, or reference data as files in a git repository. Without tooling, there is no way to enforce schemas, uniqueness, foreign key relationships, or naming conventions. Errors accumulate silently until they cause production incidents.

The solution: datacur8 reads a single .datacur8 configuration file that declares your data types, schemas, and constraints. It validates every file deterministically, produces clear error messages, and runs identically locally and in CI.

Key Features

• JSON Schema validation — validate every data file against an inline JSON Schema with full-featured support via google/jsonschema-go
• Cross-file constraints — enforce uniqueness, foreign keys, and path-to-attribute rules across files and types
• Export — compile validated data into deterministic JSON, YAML, or JSONL output files
• Tidy — normalize formatting (sorted keys, stable ordering) for clean diffs
• Strict mode — optionally enforce additionalProperties: false on all object schemas
• CSV support — schema-guided type conversion with header validation
• Git-friendly — designed for CI pipelines; identical results locally and in automation

Quick Start

1. Create a .datacur8 file in your repository root:

   version: "1.0.0"
   types:
     - name: team
       input: yaml
       match:
         include:
           - "^teams/.*\\.ya?ml$"
       schema:
         type: object
         required: ["id", "name"]
         properties:
           id: { type: string }
           name: { type: string }
         additionalProperties: false
       constraints:
         - type: unique
           key: "$.id"
   

2. Run validation:

   datacur8 validate
   

3. Export compiled outputs:

   datacur8 export
   

4. Normalize formatting:

   datacur8 tidy