Scraping Data Pipeline

소개

Streamlines the multi-stage process of collecting and managing UFC fighter information, including personal stats, fight histories, and career records. This skill guides users through a structured pipeline that crawls alphabetical fighter listings, extracts deep details into JSON/JSONL formats, performs rigorous data validation, and handles ingestion into PostgreSQL or SQLite databases. It is particularly useful for maintaining data integrity in sports analytics applications, providing specific workflows for full refreshes, sample testing, and recovering from failed scraping attempts while managing Redis cache invalidation.

주요 기능

• Support for both production PostgreSQL environments and lightweight SQLite development.
• End-to-end pipeline management from raw web crawling to database ingestion.
• Built-in cache management workflows for Redis-backed backend services.
• 2 GitHub stars
• Dual-stage scraping logic for alphabetical fighter lists and granular career details.
• Integrated data validation for JSONL structures and record count verification.

사용 사례

• Executing a full data refresh of the UFC Pokedex database from official sources.
• Identifying and re-scraping missing fighter records to ensure data completeness.
• Creating sample datasets for local development and API testing using SQLite fixtures.