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json5.5 KB

Willow Event Kitchen: Cost of goods sold document

Cost of goods sold document for Willow Event Kitchen. The same statement as JSON, listing all 24 purchase ids it consumed so the total can be checked against the shared purchases table by id. Totals: opening 1100.18, purchases 1886.41, closing 879.96, cost of goods sold 2106.63 CAD.

json

application/json

5.5 KB
Kit
restaurant-catering
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-catering
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
Lines
6
Purchase Rows In Period
24
Cogs Cad
2106.63
Encoding
UTF-8

Testing contract

Expected to pass
Scenario
Use cost of goods sold document in the Willow Event Kitchen costing and daily operations workflow.
Expected result
The same statement as JSON, listing all 24 purchase ids it consumed so the total can be checked against the shared purchases table by id. Totals: opening 1100.18, purchases 1886.41, closing 879.96, cost of goods sold 2106.63 CAD.

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

“Willow Event Kitchen: Cost of goods sold 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("cogs-report.json") as f:
    data = json.load(f)
print(type(data), len(data))

Generated by generation/restaurant_costing.py. Free for any use, no attribution required, license.