Copper Oven Bakery — Workflow and import guide
Workflow and import guide for Copper Oven Bakery. Documents 18 source tables, exact expected totals and supported versus manual workflows.
# Copper Oven Bakery
A fictional bakery with eight weeks of comparable supplier purchases, recipe planning, stock balances, menu sales and guest feedback.
All entities and transactions are fictional. Snapshot: 2026-09-08.
## Start with the linked model
1. Open operations.xlsx and review the Summary sheet. Source tables contain stable primary keys and declared references.
2. Change source values in the workbook to explore the calculated totals and editable chart.
3. Compare output values with expected-results.json. The JSON Schema checks types; primary-key uniqueness and joins also require the listed semantic checks.
4. Use invalid-records.json only in a local validation harness. Its three cases exercise duplicate keys, wrong types and an orphan reference.
## Included tables
| Table | Records | Primary key |
| --- | ---: | --- |
| purchases | 96 | purchase_id |
| suppliers | 2 | supplier_id |
| ingredients | 6 | ingredient_id |
| locations | 1 | location_id |
| menu | 4 | menu_item_id |
| recipes | 11 | recipe_line_id |
| sales | 56 | sales_id |
| inventory | 6 | count_id |
| reviews | 18 | review_id |
| modifiers | 8 | modifier_id |
| sales_modifiers | 12 | applied_id |
| refunds | 8 | refund_id |
| guests | 12 | guest_id |
| reservations | 12 | reservation_id |
| staff | 8 | employee_id |
| staffing_shifts | 56 | shift_id |
| inventory_movements | 48 | movement_id |
| menu_price_changes | 4 | price_change_id |
## Expected totals
- Supplier spend: 10,172.83 CAD. Sum purchases.total_price over the included records.
- Net menu sales: 6,651.14 CAD. Sum sales.net_sales over the included records.
- Menu items sold: 1,316.00 items. Sum sales.quantity over the included records.
- Guest reviews: 18.00 records. Count unique review_id values in reviews.
## Novus Restaurant imports
The purchase CSV, TSV and first worksheet of operations.xlsx share the supported purchase columns. Select one representation for a fresh organization. Purchase and review imports require a signed-in non-viewer account and confirmation that you hold the data rights.
Import purchases.csv first: 96 accepted rows, two suppliers and six items. Each supplier/item history contains eight dated observations. An equivalent TSV or XLSX replay is expected to add zero rows. Upload reviews.csv through the separate review-import action: 18 accepted reviews.
invalid-purchases.csv and invalid-reviews.csv contain four rejected rows each. An all-invalid purchase run currently returns HTTP 201 with a failed run status. Missing/non-numeric numeric fields have permissive legacy coercion in the current importer; these tests use explicit unsupported dates, missing required names and out-of-range amounts/ratings.Specifications
- Kit
- restaurant-bakery
- Industry
- food-service
- Schema Version
- 1
- Synthetic
- true
- As Of
- 2026-09-08
Testing contract
Reference control- Scenario
- Use workflow and import guide in the Copper Oven Bakery purchasing, price-history, menu-costing, inventory, sales, reviews, modifiers, refunds, reservations, staffing, stock-movements, menu-pricing workflow.
- Expected result
- Documents 18 source tables, exact expected totals and supported versus manual workflows.
What is a .md file?
Markdown (MD) is a lightweight plain-text markup language that uses simple punctuation conventions to denote headings, lists, links, emphasis, and code. It is designed to be readable as-is and to convert cleanly to HTML. It is widely used for documentation, READMEs, and content authoring.
How to use this file
Use an example Markdown file to test parsers and renderers, verify GitHub-Flavored Markdown extensions like tables and fenced code, and exercise HTML-conversion pipelines.
How to use this file for testing
“Copper Oven Bakery — Workflow and import guide” is a deterministic Novus Examples fixture for Data import. Realistic faker-generated datasets with documented schemas for testing import and ETL flows.
Documented properties for this file: MD · 4,119 bytes. Compare results against paired or grouped companions on this page when present (clean↔damaged, searchable↔scanned, or format twins) so scores stay reproducible across runs.
Download the file once, keep the path stable in CI or local scripts, and treat the spec table as the contract: dimensions, seeds, field lists, and roles are intentional. Corrupt or invalid samples are labelled as such, expect parsers to fail loudly rather than silently accept them.
Document fixtures list their internal structure (pages, fields, tracked changes, embedded objects) in the spec table. Test extractors, converters, and OCR against that known structure, and compare searchable↔scanned or format-twin companions when present.
Code examples
import markdown # pip install markdown
html = markdown.markdown(open("guide.md").read())
print(html[:200])Related files
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