Ember Lane Pizza: Plate costing document
Plate costing document for Ember Lane Pizza. A nested JSON document with 4 dishes and 18 recipe lines, an ingredient block carrying the pack and reference purchase for every price, and an explicit roundingRules object naming the decimal places used at each step.
json
application/json
- Kit
- restaurant-pizzeria
- 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-pizzeria
- Industry
- food-service
- Schema Version
- 1
- Synthetic
- true
- As Of
- 2026-09-08
- Currency
- CAD
- Period Start
- 2026-08-20
- Period End
- 2026-09-02
- Dishes
- 4
- Recipe Lines
- 18
- Encoding
- UTF-8
Testing contract
Expected to pass- Scenario
- Use plate costing document in the Ember Lane Pizza costing and daily operations workflow.
- Expected result
- A nested JSON document with 4 dishes and 18 recipe lines, an ingredient block carrying the pack and reference purchase for every price, and an explicit roundingRules object naming the decimal places used at each step.
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
“Ember Lane Pizza: Plate costing document” 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. 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("plate-costs.json") as f:
data = json.load(f)
print(type(data), len(data))Related files
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Generated by generation/restaurant_costing.py. Free for any use, no attribution required, license.