A scraping script that works today and silently breaks next Tuesday is worse than no script at all — because someone downstream is still trusting its output. At Codefumes we treat scraping as a data engineering problem, not a quick hack.
Separate fetching from parsing
The single biggest reliability win: never parse HTML in the same step that downloads it. Store the raw response first, then parse from storage. When a site changes its markup, you re-run the parser over archived pages instead of losing a week of data.
Design for failure
Every target site will eventually rate-limit you, change its DOM, or move behind a login. A production pipeline needs:
- Retry with backoff — transient errors should never surface as missing data.
- Schema validation — parsed records get validated before they enter the dataset, so a layout change produces a loud alert instead of quiet garbage.
- Monitoring on volume — if yesterday produced 10,000 records and today produced 400, someone should get paged.
Be a good citizen
Respecting robots.txt, honouring rate limits, and identifying your crawler are not just ethics — they’re operational strategy. Polite crawlers get blocked less, run longer, and keep the data flowing.
Structured output is the product
Clients don’t want HTML — they want answers. The last mile of every pipeline we build is a clean, documented schema: typed fields, consistent units, explicit nulls. The difference between a scraper and a data product is entirely in this step.