Bespoke Scraper Framework
When you’re building domain corpora, the scraper is the front line. A simple BeautifulSoup script isn’t enough when you’re pulling structured data from thousands of pages across docs sites, PDFs, KBs, and APIs. That’s why I design YAML-driven scraper frameworks in Python — built for repeatability, scale, and resilience.
My Approach
Config-Driven Jobs – YAML templates define targets, selectors, and update rules, so new scrape jobs can be deployed in minutes without rewriting code.
Headless Browser Support – Captures JS-rendered pages and dynamic content (think modern docs sites) without breaking pipeline flow.
Rate Limiting & Retry Logic – Built-in throttling, exponential backoff, and error logging for stability at scale.
Diff-Based Updates – Instead of re-pulling entire sites, the framework only captures changes, reducing load and keeping datasets fresh.
Anti-Duplication Hashing – Checksums and content hashes prevent redundant ingestion across runs.
Advancing Further
The framework is extensible. I continue to expand it toward:
API Integration – direct pulls from vendor knowledge bases and private data sources.
Job Orchestration – containerized jobs with scheduling and monitoring (Docker/K8s ready).
Smart Error Recovery – automated pattern detection when DOM structures change, minimizing manual fixes.
Why It Matters
Data pipelines live or die at the scraping layer. With frameworks like this, I don’t just grab documents — I create a reliable feed of structured, versioned, and de-duplicated data. That reliability translates directly into better embeddings, faster refresh cycles, and more trustworthy downstream AI systems.
