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

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

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