factorylint 0.1.7

A CLI tool to lint Azure Data Factory resources for naming convention compliance

🏭 FactoryLint

FactoryLint is a Python CLI tool for linting Azure Data Factory (ADF) resources to ensure they follow consistent, enforceable naming conventions.

It validates pipelines, datasets, linked services, and triggers using a fully configurable rules file, making it ideal for CI/CD pipelines (Azure DevOps, GitHub Actions) and team-wide governance.

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✨ Features

• ✅ Lint ADF resources:
  • Pipelines
  • Datasets
  • Linked Services
  • Triggers
• ⚙️ Fully configurable rules via YAML or JSON
• 🧠 Automatic ADF resource type detection
• 📊 Clear, colorized terminal output
• 💾 Machine-readable JSON report output
• 🚀 Designed for CI/CD usage
• 🛠 Simple, predictable CLI interface

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📦 Installation

Install from PyPI

pip install factorylint

Local development install

git clone https://github.com/DimaFrank/FactoryLint.git
cd FactoryLint
pip install -e .

🚀 Usage

Initialize project (optional)

Creates the .adf-linter directory used for repo

factorylint init

Lint ADF resources

Run linting against a directory containing ADF resources.

factorylint lint --config ./config.yml --resources .

Option	Description
--config	Path to rules configuration file (YAML or JSON)
--resources	Root directory containing ADF resources
--fail-fast	Stop on first error

🗂 Expected Folder Structure

FactoryLint automatically scans these subfolders under --resources:

pipeline/
dataset/
linkedService/
trigger/

Each folder may contain nested subdirectories.

📝 Configuration

The configuration file defines naming and validation rules for each ADF resource type.

Supported formats: YAML (.yml, .yaml) or JSON

The config is validated before linting starts

Invalid configs fail the run immediately (CI-safe)

Example config.yml

Pipeline:
  enabled: true
  general_rules:
    min_parts: 3
    description_required: false
  types:
    master:
      naming:
        prefix: "PL_M_"
        case: upper
        separator: "_"
        pattern: "^PL_M_[A-Z0-9_]+$"
    sub:
      naming:
        prefix: "PL_S_"
        case: upper
        separator: "_"
        pattern: "^PL_S_[A-Z0-9_]+$"

Dataset:
  enabled: true
  naming:
    prefix: "DS_"
    case: upper
    separator: "_"
    pattern: "^DS_[A-Z0-9_]+$"
    min_separated_parts: 3
    max_separated_parts: 6
    allowed_formats:
      - PARQ
      - CSV
    allowed_source_abbreviations:
      AzureBlob: ABLB
      ADLS: ADLS

LinkedService:
  enabled: true
  naming:
    prefix: "LS_"
    case: upper
    separator: "_"
    min_separated_parts: 2
    max_separated_parts: 4
    allowed_abbreviations:
      - ABLB
      - ADLS

Trigger:
  enabled: true
  naming:
    prefix: "TR_"
    case: upper
    separator: "_"
    min_separated_parts: 3
    max_separated_parts: 5
    allowed_types:
      - SCH
      - EVT

📊 Output

Terminal output

FactoryLint provides clear, colorized feedback:

❌ dataset/DS_INVALID_NAME.json
   - Dataset 'DS_INVALID_NAME' does not match pattern '^DS_[A-Z0-9_]+$'
✅ pipeline/PL_M_LOAD_CUSTOMERS.json

JSON report

All linting errors are saved to:

.adf-linter/linter_results.json

Example:

{
  "dataset/DS_INVALID_NAME.json": [
    "Dataset 'DS_INVALID_NAME' does not match pattern '^DS_[A-Z0-9_]+$'"
  ]
}

🔁 CI/CD Usage (Azure DevOps example)

- task: UsePythonVersion@0
  inputs:
    versionSpec: '3.x'

- script: |
    pip install factorylint
    factorylint lint --config ./config.yml --resources .
  displayName: 'Run FactoryLint'

• Exit code 1 if errors are found

• Perfect for gating PRs and enforcing standards

🧠 Design Principles

• ❌ No hardcoded paths

• ❌ No assumptions about project layout outside --resources

• ✅ Fully installable CLI

• ✅ Deterministic behavior in CI

• ✅ Clear separation of CLI and core logic

📝 License

This project is licensed under the MIT License. See the LICENSE file for details.