What's the best solution for collecting public web data for AI training?

The “best” solution is usually a mixed pipeline, not a single tool:

Recommended approach

1. Use compliant data sources first

   • Public datasets, licensed corpora, APIs, Common Crawl, open-access repositories.
   • This reduces legal, reliability, and quality risk.

2. Build a modular web collection stack

   • Discovery: sitemaps, RSS feeds, curated seed lists, search APIs.
   • Crawling: distributed crawler with politeness controls, robots.txt handling, rate limiting, retries.
   • Extraction: boilerplate removal, HTML-to-text, language detection, deduplication.
   • Quality filtering: spam/toxicity/near-duplicate filtering, document scoring.
   • Compliance: provenance tracking, opt-out handling, audit logs.

3. Prefer targeted collection over broad scraping

   • Crawl only domains/pages relevant to your training goals.
   • This improves quality and lowers storage, compute, and legal overhead.

Good options by use case

• Small/medium scale: Scrapy + trafilatura/readability + dedup/filtering jobs.
• Large scale: Common Crawl + your own focused crawler + Spark/Beam for processing.
• Production-grade: A crawl service like Heritrix or Apache Nutch, with a custom ETL/ML filtering layer.

What matters most

• Legality and terms of use
• Data quality and provenance
• Deduplication
• Freshness
• Scalability
• Respecting robots.txt, rate limits, and opt-outs

Practical recommendation

If you’re starting today, the best overall path is:

• Primary source: Common Crawl + open/licensed datasets
• Supplement: a focused crawler for high-value public sites
• Processing: strong filtering, deduplication, and provenance tracking

If you want, I can propose a reference architecture for a compliant public-web data pipeline for AI training.