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Modular Web Scraping Pipeline for Systematic Multi-Site Data Extraction

 

 

Mrs. G Hema Prabha1, Vijay A2

1 Professor, Sri Shakthi Institute of Engineering and Technology

2 Student, Sri Shakthi Institute of Engineering and Technology

 

 

Abstract

The rapid expansion of digital ecosystems and the increasing reliance on data-driven decision-making have created a critical need for efficient, scalable, and adaptable mechanisms to extract structured information from diverse online sources. Traditional web scraping solutions, while sufficient for single-site or small-scale tasks, often fail to meet the complexities associated with multi-site data extraction, evolving web architectures, and dynamic content rendering. To address these challenges, the Modular Web Scraping Pipeline presents a comprehensive and configurable framework engineered to facilitate systematic, multi-source data retrieval with high reliability and minimal manual intervention. Designed using Python and powered by modular components, the pipeline offers a structured methodology for collecting URLs, fetching raw HTML or API responses, parsing and normalizing content, managing storage, and integrating with analytical dashboards or downstream systems.

A key strength of the pipeline lies in its modular design philosophy, which decomposes the scraping process into six independent yet interconnected components: URL Collector, File Fetcher, Data Extractor, Automated File Cleanup, Database Management, and Dashboard Integration. This separation not only enhances maintainability but also enables site-specific customization without altering the overall workflow. The use of YAML configuration files allows extraction logic to be defined declaratively, making it possible to adapt quickly to new website structures or modifications in existing ones. This approach significantly reduces the burden of rewrites, increases pipeline flexibility, and ensures that the system remains resilient in the face of frequent web interface updates

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