End-to-End Scraping Pipeline

This lesson ties everything together.

Goal: One system that: - fetches pages - parses data - cleans fields - stores in DB - logs progress - runs on schedule

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High-level pipeline

```mermaid flowchart LR A[Scheduler] --> B[Fetcher] B --> C[Parser] C --> D[Cleaner] D --> E[DB Store] B --> F[Logger] C --> F D --> F E --> F ```

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Minimal pipeline code

```python def run(): html = fetch_with_retry(URL) items = parse(html)

for item in items: clean = clean_item(item) upsert_product( clean["name"], clean["price"], clean["url"] )

if __name__ == "__main__": run() ```

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Why this matters This structure: - survives crashes - avoids duplicates - easy to maintain - ready for scale

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Real-world usage - price monitoring - job listings - news aggregation - market research - analytics feeds

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Remember - Always think in pipeline - Each step has one job - This is how professional scrapers are built