Scraping 1M+ Data Points

The Challenge

The client had a team of data-entry operators manually collecting marketing intelligence data from dozens of online sources each day. The process was slow, error-prone, and expensive — and the data was always hours behind by the time it reached analysts.

They needed a way to collect structured, clean data at scale, automatically, and in near real-time.

The Solution

I built a multi-stage automated scraping pipeline using Python that:

• Crawled and extracted data from multiple target websites on a scheduled basis
• Cleaned, normalized, and de-duplicated records using Pandas
• Stored processed data in a PostgreSQL database with indexed lookups
• Generated daily structured reports automatically delivered to stakeholders
• Included error-handling, retry logic, and proxy rotation to maintain uptime

The Results

The client decommissioned their manual data-entry team within 30 days of deployment. Data quality improved significantly due to eliminating human errors, and analysts now receive fresh data every morning without lifting a finger.

Tech Stack