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HR — Employees Mini (JSON)

JSON twin of the hr/employees-mini mini-dataset.

Data importJSON parsing
Preview — first 50 linesjson
[
  {
    "id": "E001",
    "role": "engineer",
    "dept": "platform",
    "fte": 1.0
  },
  {
    "id": "E002",
    "role": "designer",
    "dept": "product",
    "fte": 1.0
  },
  {
    "id": "E003",
    "role": "pm",
    "dept": "product",
    "fte": 1.0
  },
  {
    "id": "E004",
    "role": "analyst",
    "dept": "data",
    "fte": 0.8
  },
  {
    "id": "E005",
    "role": "support",
    "dept": "cx",
    "fte": 1.0
  },
  {
    "id": "E006",
    "role": "intern",
    "dept": "platform",
    "fte": 0.5
  },
  {
    "id": "E007",
    "role": "engineer",
    "dept": "data",
    "fte": 1.0
  },
  {
    "id": "E008",
    "role": "recruiter",
    "dept": "people",
    "fte": 1.0
  }
]
… 51 lines total — download for the full file.

Specifications

Records
8
Schema
id, role, dept, fte

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

“HR — Employees Mini (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: id, role, dept, fte. 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("employees-mini.json") as f:
    data = json.load(f)
print(type(data), len(data))

Related files

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