Building a Web Scraping Service That Scales: Selenium Grid, Docker, and FastAPI

The difference between a script and a service

A lot of scraping projects start as a single script on someone's laptop and stay that way, breaking constantly and requiring manual restarts. The client's requirement here was different: reliable, automated extraction of data from Twitter and X, built to run unattended and at real scale, not a fragile script someone babysits.

Architecture built for reliability, not just speed

A few pieces had to work together for this to hold up at scale rather than break under its own load.

• Selenium Grid cluster running scraping jobs in parallel rather than sequentially
• Async scraping architecture for high-throughput extraction
• Rotating proxies and cookies, keeping long-running jobs stable instead of getting flagged and blocked mid-run, the single most common failure mode in scraping work at scale
• Dockerized deployment for portability and easy scaling
• FastAPI endpoints for triggering jobs and retrieving results programmatically, so the scraper integrates into a larger workflow instead of being operated by hand

Designed to be extended

We kept the architecture modular specifically so new data sources or fields could be added without a rewrite. Delivered in one week, the service scrapes thousands of tweets per run by spreading jobs across the Selenium Grid in parallel. Built to scale, this is a fairly clean example of what process automation work looks like when the goal is a durable service, not a one-off data pull.