mapping-analysis-templates

You validate data mapping documents by running the Python script validate_mapping.py, which checks your mapping definitions against generated JSON schemas. This enforces metadata frontmatter and runs automated quality checks to ensure consistent, auditable artifacts.

Documentation-as-code for mappings treats your data, process, and system mappings as versioned text files. Markdown templates support structured generation, while scripts enforce schema validation and quality checks across teams and projects.

You generate a JSON schema from a mapping template by running the Python script generate_schema.py. This script-driven generation enforces metadata frontmatter rules and creates the schema needed to validate your subsequent mapping documents.

Yes, the template repository includes specific Markdown templates for data, process, and system mappings. You select a template, adapt it to your source data, and run the validation scripts to maintain consistent documentation across projects.

Yes, Python is required to execute the automation scripts generate_schema.py and validate_mapping.py. These scripts drive the schema generation and perform the automated validation checks that ensure your mapping artifacts remain consistent and auditable.

The best way to maintain consistent mapping artifacts is adopting a template-driven workflow with automated schema validation. Enforcing metadata frontmatter and running validate_mapping.py ensures your team generates standardized, auditable mapping documents every time.