---
name: mock-data-7bd188e83aa0
description: Generate deterministic synthetic CSV files under data/sandbox/ using the data_mock_generate MCP tool. Use when no real source file exists for a new or demo topic and you need staged data to build the download/bronze/dq notebook pipeline against.
---

# mock-data

## When to use

- A new topic has no real source file yet and you need something to ingest
- A demo needs repeatable, PII-free staged data
- You are writing or testing a Bronze notebook and need a staged CSV with a specific shape

Do not use real source extracts. Always generate synthetic data and run it through the normal masking and DQ pipeline.

## Tool — `data_mock_generate` (MCP)

Call the `data_mock_generate` MCP tool on the `fabric-server`. It runs inside
the container, so pass `target_dir` = the project root mounted into the
container; output defaults to `data/sandbox/<topic>.csv` under it. Always pass
a `schema` derived from the target table.

| Argument | Default | Notes |
|---|---|---|
| `target_dir` | — | Project root (required); the container writes under it |
| `topic` | `orders` | Output file stem |
| `rows` | `1000` | Row count |
| `seed` | `42` | Deterministic seed |
| `engine` | `stdlib` | One of `stdlib`, `faker`, `mimesis`, `sklearn` |
| `schema` | — | Inline JSON array of `{name,type}` column defs |
| `schema_file` | — | Project-relative path to a JSON schema (mutually exclusive with `schema`) |
| `output` | — | Explicit project-relative CSV path |

Examples (arguments shown as JSON for clarity):

```jsonc
// Custom schema matching your target table
{"target_dir": ".", "topic": "orders", "rows": 1000,
 "schema": "[{\"name\":\"id\",\"type\":\"id\"},{\"name\":\"customer_id\",\"type\":\"int\",\"min\":1,\"max\":500},{\"name\":\"email\",\"type\":\"email\"},{\"name\":\"amount\",\"type\":\"decimal\",\"min\":2.5,\"max\":2500,\"decimals\":2},{\"name\":\"order_date\",\"type\":\"date\",\"start\":\"2025-01-01\",\"end\":\"2025-12-31\"}]"}

// Schema from a reusable JSON file
{"target_dir": ".", "topic": "orders", "rows": 5000, "schema_file": "schemas/orders.json"}

// Faker engine for richer PII-shaped values
{"target_dir": ".", "engine": "faker", "topic": "customers", "rows": 1000,
 "schema": "[{\"name\":\"id\",\"type\":\"id\"},{\"name\":\"full_name\",\"type\":\"name\"},{\"name\":\"email\",\"type\":\"email\"},{\"name\":\"phone\",\"type\":\"phone\"},{\"name\":\"address\",\"type\":\"address\"}]"}

// ML fixtures — feature columns + a target column
{"target_dir": ".", "engine": "sklearn", "rows": 5000,
 "schema": "[{\"name\":\"id\",\"type\":\"id\"},{\"name\":\"price\",\"type\":\"float\",\"decimals\":4},{\"name\":\"quantity\",\"type\":\"float\",\"decimals\":4},{\"name\":\"target\",\"type\":\"int\"}]"}
```

Output lands at `data/sandbox/<topic>.csv`.

## Schema column types

| Type | Options | Notes |
|---|---|---|
| `id` | — | Sequential integer starting at 1 |
| `int` | `min`, `max` | Random integer |
| `float` / `decimal` | `min`, `max`, `decimals` | Random float |
| `string` / `word` / `str` | — | Random word |
| `sentence` / `text` | — | Random sentence |
| `name` | — | Full name |
| `first_name` / `last_name` | — | Name parts |
| `email` | — | Email address |
| `address` | — | Street address |
| `date` | `start`, `end` | ISO date (YYYY-MM-DD) |
| `datetime` / `timestamp` | `start`, `end` | ISO datetime |
| `boolean` | — | True or False |
| `uuid` | — | UUID v4 |
| `phone` | — | Phone number |
| `company` | — | Company name |
| `url` | — | URL |

## Engine selection guide

| Engine | Install | When to use |
|---|---|---|
| `stdlib` (default) | none | No-dependency fallback; string types produce placeholders (`name_1`, `user.1@example.test`) |
| `faker` | `pip install Faker` | Realistic PII-shaped values for UI tests or simple databases |
| `mimesis` | `pip install mimesis` | Very high-volume generation (millions of rows) at speed |
| `sklearn` | `pip install scikit-learn` | Controlled ML classification fixtures with known class balance |

## After generating

The staged CSV still needs the full three-notebook pipeline:

1. `download_<source>.py` — skip if file already staged; otherwise simulate a fetch
2. `bronze_<source>.py` — ingest only new files; mask any PII-shaped columns
3. `dq_bronze_<source>.py` — Great Expectations checks on the Bronze table

Synthetic PII-shaped columns (`email`, `name`, `address`, `phone`) must go through the same masking barrier as real data.


---
Source attribution: scardoso-lu
Original project: https://github.com/scardoso-lu/fabric-skills-settings/blob/33534eb185fda573125308a0129457bd65419d13/server/skills/mock-data/SKILL.md
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2026 Silvio Cardoso

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: scardoso-lu
Original project: https://github.com/scardoso-lu/fabric-skills-settings/blob/33534eb185fda573125308a0129457bd65419d13/server/skills/mock-data/SKILL.md
Attribution basis: Source repository record

Fields of work: Data & Analytics, Development & Engineering
Roles: Backend Developer, Data Analyst, Data Scientist
Specialties: API, Database
Tools: Data_Mock_Generate
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: 49b100e477c4b1e479076b0f51ebdedebd64c77256975690004864e1e5b13b6e