Hey everyone! I’m working on a cloud project where I need to extract job listings from career websites. My current plan involves using AWS Lambda to run a headless Chrome browser that will collect the data dynamically, then store everything in S3. The thing is, I’m not sure about the best way to make this solution handle larger volumes of data efficiently. What would be your recommended approach for something like this? Are there any AWS services or patterns that work better for scaling web scraping operations? I’m pretty new to cloud architecture so any advice would be helpful!
Had the same issue last year scraping product data at scale. Proxy rotation through AWS saved me - set up multiple NAT gateways across regions and rotate requests through them. Use EventBridge to schedule scraping jobs instead of running them continuously. Write to RDS first for structured data, then archive to S3. The real game changer was CloudWatch alarms monitoring scraping success rates and auto-pausing jobs when sites started blocking. Kept my IP ranges from getting permanently blacklisted.
step functions could work well here - run multiple lambda functions in parallel instead of one massive scraper. job listings don’t change much, so batch processing is perfect. if you need more control than lambda but want to avoid managing containers, try elastic beanstalk.
Lambda’s 15-minute timeout kills most web scraping projects, so skip it. ECS Fargate is way better - you get proper resource control and can run longer scraping sessions. Throw SQS into the mix for task queuing and spread the work across multiple containers. Don’t forget rate limiting and user-agent rotation or you’ll get blocked fast. I use DynamoDB to track progress and catch duplicates. This setup’s handled large scraping jobs without issues.