POS SKU Catalog (JSON)
A point-of-sale SKU catalog with tax codes and active flags — fictional Meridian Supply inventory. JSON twin.
[
{
"sku": "SKU-1001",
"name": "Wireless Mouse",
"category": "peripherals",
"price_cents": 1250,
"tax_code": "TAX-A",
"active": true
},
{
"sku": "SKU-1002",
"name": "USB-C Hub",
"category": "peripherals",
"price_cents": 2900,
"tax_code": "TAX-A",
"active": true
},
{
"sku": "SKU-1003",
"name": "Cable Organiser",
"category": "accessories",
"price_cents": 320,
"tax_code": "TAX-B",
"active": true
},
{
"sku": "SKU-1004",
"name": "Notebook A5",
"category": "stationery",
"price_cents": 450,
"tax_code": "TAX-B",
"active": true
},
{
"sku": "SKU-1005",
"name": "Desk Mat",
"category": "accessories",
"price_cents": 1800,
"tax_code": "TAX-A",
"active": false
},
{
"sku": "SKU-1006",
"name": "Noise-cancelling Headphones",
"category": "audio",
"price_cents": 7999,
"tax_code": "TAX-A",
"active": true
},
{Specifications
- Records
- 8
- Schema
- sku, name, category, price_cents, tax_code, active
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
“POS SKU Catalog (JSON)” is a deterministic Novus Examples fixture for Data import, JSON parsing. Realistic faker-generated datasets with documented schemas for testing import and ETL flows.
Documented properties for this file: 8 records · schema: sku, name, category, price_cents, tax_code, active. 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("sku-catalog.json") as f:
data = json.load(f)
print(type(data), len(data))Related files
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Generated by generation/data_domains_wave_c.py. Free for any use, no attribution required — license.