Airflow Etl

Document Inventory:

• Identify document types (PDF, DOC, DOCX, etc.)
• Understand the URL patterns for documents
• Determine how to detect new documents
• Note any metadata available (dates, categories, titles)

Phase 2: Generate Airflow Pipeline Assets

Create a complete, production-ready Airflow project structure:

airflow_pipelines/
├── dags/
│   └── [source_name]_etl_dag.py
├── operators/
│   ├── __init__.py
│   ├── document_scraper.py
│   └── document_converter.py
├── utils/
│   ├── __init__.py
│   ├── license_checker.py
│   └── file_manager.py
├── config/
│   └── [source_name]_config.yaml
├── requirements.txt
└── README.md

File Generation Requirements:

1. DAG File (dags/[source_name]_etl_dag.py):

• Daily schedule (adjustable)
• Clear task dependencies
• Error handling and retries
• Sensor for checking new documents
• Download task
• Conversion task to markdown
• File organization task
• Use Airflow best practices (XComs, task groups, dynamic task generation)

2. Document Scraper (operators/document_scraper.py):

• BeautifulSoup or Scrapy for web scraping
• Request handling with retries
• Respect robots.txt
• User-agent configuration
• Rate limiting
• Checksum/hash tracking to avoid re-downloading
• State management for incremental updates

3. Document Converter (operators/document_converter.py):

• Support for PDF, DOC, DOCX conversion to markdown
• Use libraries like pypandoc, pdfplumber, or python-docx
• Preserve document structure (headings, lists, tables)
• Extract metadata
• Handle encoding issues
• Clean and normalize output

4. License Checker (utils/license_checker.py):

• Validate license information
• Check for commercial use permission
• Log license status
• Skip non-compliant documents

5. File Manager (utils/file_manager.py):

• Create meaningful directory structure
• Organize by date, category, or document type
• Generate consistent filenames
• Handle duplicates
• Maintain index of processed documents

6. Configuration (config/[source_name]_config.yaml):