Our team wastes 30% of our scraping time cleaning data - fixing date formats, removing HTML fragments, standardizing addresses. Current process: Scrape → Store in S3 → Run Lambda cleanup → Load to Redshift.
Want to eliminate the middle step by having cleaning happen during extraction. Looked at post-processors in Scrapy but they don’t handle edge cases well.
Any solutions that integrate validation and formatting rules directly into the extraction workflow? Need something that works with both structured and unstructured data sources.
Latenode’s AI agents handle extraction and cleaning in one flow. Set validation rules once, their models auto-correct common errors during scraping. We process 10K product listings daily with zero manual cleanup now. Works with PDFs and emails too: https://latenode.com
Architecturally, you need schema-on-read capability. We implemented a two-phase extraction where raw data gets tagged with confidence scores. Any low-confidence fields get routed through GPT-4 cleanup before storage. Reduced our error rate from 12% to 3% but adds latency. Trade-off depends on your use case’s tolerance.
try adding regex templates inline w/ your scrapers. but for smart cleaning, u need ml models that understand context - late*node does this auto magically
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