Harbour Kitchen Group: Digital menu
Digital menu for Harbour Kitchen Group. One document holding the whole front of house: 2 printed sections with 4 dishes, 8 drink lines, the set menu, the specials and the 14 entry allergen key. Every dish carries its plate cost and the paths of the two costing files it was read from, so the join back to plate-costs.csv can be checked without guessing.
json
application/json
- Kit
- restaurant-group
- Industry
- food-service
- Schema Version
- 1
- Synthetic
- true
- As Of
- 2026-09-08
- Currency
- CAD
Binary json: no in-browser preview. Download it above to open in a compatible application.
Specifications
- Kit
- restaurant-group
- Industry
- food-service
- Schema Version
- 1
- Synthetic
- true
- As Of
- 2026-09-08
- Currency
- CAD
- Menu Items
- 4
- Sections
- 2
- Dishes
- 4
- Drink Lines
- 8
- Encoding
- UTF-8
- Line Endings
- LF
Testing contract
Expected to pass- Scenario
- Use digital menu in the Harbour Kitchen Group menu, allergen and till import workflow.
- Expected result
- One document holding the whole front of house: 2 printed sections with 4 dishes, 8 drink lines, the set menu, the specials and the 14 entry allergen key. Every dish carries its plate cost and the paths of the two costing files it was read from, so the join back to plate-costs.csv can be checked without guessing.
What is a .json file?
JSON (JavaScript Object Notation) is a lightweight, text-based data-interchange format representing objects, arrays, strings, numbers, booleans, and null. It is language-independent, human-readable, and the dominant format for web APIs and configuration. It requires a single well-formed root value.
How to use this file
Use an example JSON file to test parsers and serializers, schema validation, Unicode and number-precision handling, and API request or response processing.
How to use this file for testing
“Harbour Kitchen Group: Digital menu” is a deterministic Novus Examples fixture for Data import, JSON parsing, Schema validation. Realistic faker-generated datasets with documented schemas for testing import and ETL flows.
Documented properties for this file: UTF-8 · LF. 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.
Data fixtures document their exact quirks (delimiters, encodings, null handling, schema, and row counts) in the spec table. Point your parser or importer at the file and assert it handles the documented edge cases; clean and deliberately-messy siblings make before/after diffs straightforward.
Code examples
import json
with open("digital-menu.json") as f:
data = json.load(f)
print(type(data), len(data))Related files
- jsonAlder Table Bistro: Digital menuDigital menu for Alder Table Bistro. One document holding the whole front of house: 3 printed sections with 4 dishes, 8 drink lines, the set menu, the specials and the 14 entry allergen key. Every dish carries its plate cost and the paths of the two costing files it was read from, so the join back to plate-costs.csv can be checked without guessing.
- jsonldAlder Table Bistro: Menu as schema.org JSON-LDMenu as schema.org JSON-LD for Alder Table Bistro. A schema.org Menu document with 7 MenuSection nodes, 5 of them top level, and 27 MenuItem nodes carrying 27 Offer nodes, each with price as a JSON number and priceCurrency CAD. Modifiers are menuAddOn MenuItems and every declared allergen travels as an additionalProperty PropertyValue. This is the structured data shape a search engine reads, and it is rare as a downloadable fixture.
- jsonCedar Street Tacos: Digital menuDigital menu for Cedar Street Tacos. One document holding the whole front of house: 2 printed sections with 4 dishes, 8 drink lines, the set menu, the specials and the 14 entry allergen key. Every dish carries its plate cost and the paths of the two costing files it was read from, so the join back to plate-costs.csv can be checked without guessing.
- jsonldCedar Street Tacos: Menu as schema.org JSON-LDMenu as schema.org JSON-LD for Cedar Street Tacos. A schema.org Menu document with 6 MenuSection nodes, 4 of them top level, and 27 MenuItem nodes carrying 27 Offer nodes, each with price as a JSON number and priceCurrency CAD. Modifiers are menuAddOn MenuItems and every declared allergen travels as an additionalProperty PropertyValue. This is the structured data shape a search engine reads, and it is rare as a downloadable fixture.
- jsonCopper Oven Bakery: Digital menuDigital menu for Copper Oven Bakery. One document holding the whole front of house: 2 printed sections with 4 dishes, 8 drink lines, the set menu, the specials and the 14 entry allergen key. Every dish carries its plate cost and the paths of the two costing files it was read from, so the join back to plate-costs.csv can be checked without guessing.
- jsonldCopper Oven Bakery: Menu as schema.org JSON-LDMenu as schema.org JSON-LD for Copper Oven Bakery. A schema.org Menu document with 6 MenuSection nodes, 4 of them top level, and 30 MenuItem nodes carrying 30 Offer nodes, each with price as a JSON number and priceCurrency CAD. Modifiers are menuAddOn MenuItems and every declared allergen travels as an additionalProperty PropertyValue. This is the structured data shape a search engine reads, and it is rare as a downloadable fixture.
Generated by generation/restaurant_menus.py. Free for any use, no attribution required, license.