Scraper and data pipeline takeovers: fix and finish data collection jobs

Data pipelines move information from where it lives to where it is used. On the collection side, scrapers are often written in Python with Scrapy, Requests and Beautiful Soup, or with Playwright and Puppeteer for pages that need a browser. Jobs run on cron, GitHub Actions or orchestrators such as Airflow and Prefect, and land data in PostgreSQL, BigQuery, S3 or a spreadsheet.

What breaks in these projects

Scrapers depend on page structure, so a redesign silently breaks selectors and the job keeps running while collecting nothing. Pipelines without validation load duplicates or half-empty rows, and transformations written as one long script are hard to rerun for a single day. Scheduled jobs may live on a laptop or a forgotten server. Credentials for databases and APIs are frequently hard-coded.

What to include

Describe each source, what data you need from it and how often, and confirm that the collection respects the source's terms. Explain where the output goes and who uses it. Share the repository and a sample of good output. A repo health check gives a quick view of the codebase, and the secret leak scanner flags credentials that need moving to environment variables and rotating.