I build data pipelines that keep running: distributed crawlers, scheduled ingestion, and data lakes that turn messy third-party sources into data your business can trust. In one engagement a crawl cycle dropped from over 12 hours to under 20 minutes for more than 40,000 items.
Reduced crawl cycle time from 12+ hours to under 20 minutes for 40,000+ items—a 97% reduction in processing latency.
Architected scalable data lake processing tens of gigabytes of monthly utility telemetry seamlessly.
Engineered full data pipeline from API extraction to interactive visualization.
Built multi-portal syndication pipeline saving property managers hours of redundant data entry.
Usually. I look at scheduling, concurrency, failure handling, and rate limiting first. One crawler I rebuilt went from 12+ hours per cycle to under 20 minutes.
Python with Celery for task distribution, PostgreSQL for state, and AWS services such as Lambda and S3 where they fit.
Yes. I have built a data lake processing tens of gigabytes of utility telemetry per month.
Email a few lines on your goals, stack, and timeline. I reply within 24 hours.
Email aakashjsr@gmail.com