Data structures and meaning
Dimensional warehouse / star schema
A star schema: one fact table surrounded by dimensions, with grain declared.
ddn@1New DDN source
Why use it?
The star makes analytical queries legible — and the grain declaration prevents the classic double-count.
When it helps
Designing marts, cubes or any reporting model.
How to read it
- The fact table is events or measurements; dimensions answer who/what/where/when.
- Every fact row means exactly one thing — that is the grain.
What you need
- The grain
- The measures
- The dimensions and their levels
Things to check
- Do not mix grains in one fact table.
- Do not let a dimension quietly become a second fact table.
Scope of this diagram
Model documentation; no aggregation is computed by this view.
Work with the converted example
- Open the viewer and select
diagram. Inspect the rendered view and its source. - Open the designer. Keep the selected profile and inspect its legal palette and property controls.
- Change one displayed title or an appropriate data value. Apply the edit and check the resulting diagram.
- Run the verifier and review its diagnostics before moving complete content into review.
- Download your edited DDN source to retain the change.
The example uses the indentation-based ddn-next-1 dialect. The language guide explains reusable definitions, dotted data rows and explicit property blocks.