Schemas, sources, mappings, datasets — the data pipeline.
A schema is the shape your templates and datasets agree on. Define it once, bind many templates.
How to add a schema and its fields to a project.
Where raw data enters the system: Excel, CSV, manual grids or a plugin.
Upload a spreadsheet, pick a header row and preview the columns before committing.
Type values directly when there is no file to import.
Plugins pull live data from external systems into the pipeline automatically.
A mapping says "this column in the data source fills that field in the schema".
Step-by-step: pick a source, pick a schema, wire up the fields, preview the result.
Verify a mapping before you commit to it, and keep datasets in sync when the source changes.
The resolved, ready-to-render data that a production supplies to its templates.
Steps to create either a manual or a mapped dataset.
Trim and reshape a mapped dataset without touching the source or the mapping.
A quick decision guide.
Share a dataset with a specific user so they can edit its data without touching the template.