URLs Scraping Pipeline

Overview

Built an end-to-end URL scraping high-throughput platform to continuously discover, schedule, and scrape web pages at scale. The pipeline coordinates scraping demand, executes distributed scraping jobs, and emits structured outputs for downstream processing and monitoring.

Achievements

Deployed a reliable production pipeline that schedules scraping workloads, adapts rescrape cadence based on content change signals, and scales horizontally to handle high-volume URL inventories while reducing unnecessary rescraping.

Responsibilities

• Designed the orchestration flow that turns scraping signals into scheduled work units and manages execution windows, retries, and backpressure.
• Built distributed scraping services to fetch pages, normalize responses, and produce consistent scraping artifacts for downstream consumers.
• Optimized throughput and reliability by improving batching, error handling, and recovery logic for failed scraping attempts.
• Automated operational workflows (configuration, environment-based deployment, logging/metrics hooks) to support QA/prod parity and faster incident response.
• Implemented data quality safeguards to validate scraping outputs and prevent duplicate/invalid jobs from propagating through the pipeline.